Dr. Farshid PirahanSiah
www.pirahansiah.com
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optimize.
computing.
Scalable Verification for Safety-Critical Deep Networks
Driver Assistance Systems and Vision-Based System Validates Driver Monitoring
The present invention relates to a system for providing advertisement contents
based on facial analysis. The system consists of an image acquisition device,
a face detection module, an analysis module, a classification module, a
database, a computation module, a matching module and a display device. The
image acquisition device acquires an image of a user, the face detection
module detects the face of the user in the image, the analysis module analyses
the facial features statistically using classification models, the database
stores matching rules, weighted advertisements and a plurality of
advertisement contents and the display device displays the advertisement
contents. The computation module computes the weighted image of the user and
the matching module matches the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. The system aims to provide advertisement contents via a digital
standee by extracting salient demographic from a user to indirectly obtain
user information and behavioral preference.
The present invention relates to a system and method for providing
advertisement contents based on facial analysis using a digital standee. The
system (100) is embedded in the digital standee and comprises an image
acquisition device, a face detection module, a classification module, a data
analysis module, a computation module, a database and a matching module. The
image acquisition device is configured to acquire an image of a user, and the
face detection module uses deep learning technology to detect the user's face
in the image. The classification module classifies the user's facial features
into a plurality of classification models, such as gender, age range, emotion,
style and attention. The data analysis module obtains behavioral preference
and information of the user by analyzing the classified facial features. The
matching module matches the information with types of businesses to provide
suitable advertisement contents to the user based on rules set by the
advertisement provider. The advertisement contents are displayed on a display
device in the system.
This patent describes a system for providing advertisements based on facial
analysis. The system consists of an image acquisition device, a face detection
module, an analysis module, a computation module, a matching module, a
database, and a display device. The image acquisition device captures an image
of the user, the face detection module identifies the user's face and facial
features, the analysis module analyses the facial features using statistical
parameters and classification models, the computation module computes the
weighted image of the user, the matching module matches the weighted image of
the user with weighted advertisements, and the display device displays the
advertisement contents. The system operates in real-time and updates the
classification models continuously. The advertisement contents are based on
the user's age, gender, emotion, style, and attention and are provided by the
advertisement providers with matching rules.
The process described in this patent involves matching a user's weighted image
with a weighted advertisement based on matching rules established by the
advertisement providers. The matching rules may include order of features,
most similar features, important features, and nearest similar features. The
matching is done from left to right of the binary sequence. The selected
advertisement content is then displayed by the display device. The terms used
in the patent are defined as specified. The invention is open to changes in
form and details.
The system (100) is a device for providing advertisement contents based on
facial analysis. It consists of: an image acquisition device (10) to acquire
an image of a user, a face detection module (20) to detect the face and obtain
facial features, an analysis module (40) to analyze the facial features
statistically using classification models, a database (60) to store matching
rules and advertisements, and a display device (80) to display the selected
advertisement content. The system also has a computation module (50) to
compute a weighted image of the user based on the analyzed facial features,
and a matching module (70) to match the weighted image of the user with the
weighted advertisement to select the advertisement content. The system can
work for a single user or a group of users. The method (200) of providing
advertisement content follows similar steps as the system (100). The steps
include acquiring an image of the user, detecting the face, analyzing the
facial features, computing a weighted image of the user, obtaining matching
rules, and matching the weighted image with the weighted advertisement. The
method also includes steps of training the classification models and providing
display of the selected advertisement content.
This patent describes a system for providing advertisements based on facial
analysis using a digital standee. The system consists of an image acquisition
device, a face detection module, an analysis module, a computation module, a
matching module, a database, and a display device. The image acquisition
device captures an image of the user, the face detection module identifies the
user's face and facial features, the analysis module analyzes the facial
features using statistical parameters and classification models, the
computation module computes the weighted image of the user, the matching
module matches the weighted image of the user with weighted advertisements
based on matching rules set by the advertisement providers, and the display
device displays the advertisement contents. The system operates in real-time
and updates the classification models continuously. The advertisement contents
are based on the user's age, gender, emotion, style, and attention and are
provided by the advertisement providers with matching rules.
#
[Scalable Verification for Safety-Critical Deep
Networks](https://arxiv.org/pdf/1801.05950.pdf)
[https://arxiv.org/pdf/1801.05950.pdf](https://arxiv.org/pdf/1801.05950.pdf)
"Verifying that neural networks behave as intended may soon become a limiting
factor in their applicability to real-world, safetycritical systems such as
those used to control autonomous vehicles safety and reliability on DNNs.
verify properties of DNNs. A major challenge of verifying properties of DNNs
with satisfiability modulo theories (SMT) solvers is in handling the networks’
activation functions such as, Reluplex (domain-specific theory solvers;
through a lazy approach). 1)devising scalable verification techniques.
2)identifying design choices -> amenable to verification. "
Each neuron of a neural network computes a weighted sum of its inputs
according to learned weights. It then passes that sum through an activation
function to produce the neuron’s final output. Typically, the activation
functions introduce nonlinearity to the network, making DNNs capable of
learning arbitrarily complex functions, but also making the job of automated
verification tools much harder.
#
Driver Assistance Systems and Vision-Based System Validates Driver Monitoring
[Vision-based convolutional neural network system detects phone usage, eating,
and
drinking.](https://www.techbriefs.com/component/content/article/tb/supplements/pit/features/technology-
leaders/36144) cameras with active infrared lighting; 30 Hz and delivered
8-bit grayscale images at 1280 × 1024-pixel resolution; ResNeXt-34;
[video-based driver assistance systems, such as automated driving;
](https://www.bosch-mobility-solutions.com/en/products-and-services/passenger-
cars-and-light-commercial-vehicles/driver-assistance-systems/lane-departure-
warning/multi-purpose-camera/)resilient object detection and tracking; camera:
± 50°field of view (horizontal); +27°/ -21°field of view (vertical); > 150 m
detection range; 2.6 MP resolution.
multi path approach:
1. classifier: for pattern recognition; resilient object detection
2. dense optical flow and structure from motion; to detect static objects; 3D structure
3. deep learning: classify objects, road, edge road, orientation;
" Operation principle of the multi purpose camera: During assisted and
automated driving, the vehicle must know what is happening in its surroundings
at all times. It must reliably detect objects and people, and be able to react
to these appropriately. Here, the latest generation of the front video camera
from Bosch plays a crucial part: The multi purpose camera for assisted and
partially automated driving utilizes an innovative, high-performance system-
on-chip (SoC) with a Bosch microprocessor for image-processing algorithms. Its
unique multipath approach combines classic image-processing algorithms with
artificial-intelligence methods for comprehensive scene interpretation and
reliable object detection. With its algorithmic multipath approach and the
innovative system-on-chip, this camera generation has been specially developed
for high-performance driver assistance systems. In line with this approach,
the multi purpose camera uses for example the following technical paths at
once for image processing: The first of these is the conventional approach
already in use today. Via preprogrammed algorithms, the cameras recognize the
typical appearance of object categories such as vehicles, cyclists, or road
markings. The second and third paths are new, however. For the second path,
the camera uses the optical flow and the structure from motion (SfM) to
recognize raised objects along the roadside, such as curbs, central reserves,
or safety barriers. The motion of associated pixels is tracked. A three-
dimensional structure is then approximated based on the two-dimensional camera
image. The third path relies on artificial intelligence. Thanks to machine-
learning processes, the camera has learned to classify objects such as cars
parked by the side of the road. The latest generation can differentiate
between surfaces on the road and those alongside the road via neuronal
networks and semantic segmentation. Additional paths are used as required:
These include classic line scanning, light detection, and stereo disparity. "
[Link](https://www.bosch-mobility-solutions.com/en/products-and-
services/passenger-cars-and-light-commercial-vehicles/driver-assistance-
systems/lane-departure-warning/multi-purpose-camera/)
Scalable Verification for Safety-Critical Deep Networks
Driver Assistance Systems and Vision-Based System Validates Driver Monitoring
The present invention relates to a system for providing advertisement contents
based on facial analysis. The system consists of an image acquisition device,
a face detection module, an analysis module, a classification module, a
database, a computation module, a matching module and a display device. The
image acquisition device acquires an image of a user, the face detection
module detects the face of the user in the image, the analysis module analyses
the facial features statistically using classification models, the database
stores matching rules, weighted advertisements and a plurality of
advertisement contents and the display device displays the advertisement
contents. The computation module computes the weighted image of the user and
the matching module matches the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. The system aims to provide advertisement contents via a digital
standee by extracting salient demographic from a user to indirectly obtain
user information and behavioral preference.
The present invention relates to a system and method for providing
advertisement contents based on facial analysis using a digital standee. The
system (100) is embedded in the digital standee and comprises an image
acquisition device, a face detection module, a classification module, a data
analysis module, a computation module, a database and a matching module. The
image acquisition device is configured to acquire an image of a user, and the
face detection module uses deep learning technology to detect the user's face
in the image. The classification module classifies the user's facial features
into a plurality of classification models, such as gender, age range, emotion,
style and attention. The data analysis module obtains behavioral preference
and information of the user by analyzing the classified facial features. The
matching module matches the information with types of businesses to provide
suitable advertisement contents to the user based on rules set by the
advertisement provider. The advertisement contents are displayed on a display
device in the system.
This patent describes a system for providing advertisements based on facial
analysis. The system consists of an image acquisition device, a face detection
module, an analysis module, a computation module, a matching module, a
database, and a display device. The image acquisition device captures an image
of the user, the face detection module identifies the user's face and facial
features, the analysis module analyses the facial features using statistical
parameters and classification models, the computation module computes the
weighted image of the user, the matching module matches the weighted image of
the user with weighted advertisements, and the display device displays the
advertisement contents. The system operates in real-time and updates the
classification models continuously. The advertisement contents are based on
the user's age, gender, emotion, style, and attention and are provided by the
advertisement providers with matching rules.
The process described in this patent involves matching a user's weighted image
with a weighted advertisement based on matching rules established by the
advertisement providers. The matching rules may include order of features,
most similar features, important features, and nearest similar features. The
matching is done from left to right of the binary sequence. The selected
advertisement content is then displayed by the display device. The terms used
in the patent are defined as specified. The invention is open to changes in
form and details.
The system (100) is a device for providing advertisement contents based on
facial analysis. It consists of: an image acquisition device (10) to acquire
an image of a user, a face detection module (20) to detect the face and obtain
facial features, an analysis module (40) to analyze the facial features
statistically using classification models, a database (60) to store matching
rules and advertisements, and a display device (80) to display the selected
advertisement content. The system also has a computation module (50) to
compute a weighted image of the user based on the analyzed facial features,
and a matching module (70) to match the weighted image of the user with the
weighted advertisement to select the advertisement content. The system can
work for a single user or a group of users. The method (200) of providing
advertisement content follows similar steps as the system (100). The steps
include acquiring an image of the user, detecting the face, analyzing the
facial features, computing a weighted image of the user, obtaining matching
rules, and matching the weighted image with the weighted advertisement. The
method also includes steps of training the classification models and providing
display of the selected advertisement content.
This patent describes a system for providing advertisements based on facial
analysis using a digital standee. The system consists of an image acquisition
device, a face detection module, an analysis module, a computation module, a
matching module, a database, and a display device. The image acquisition
device captures an image of the user, the face detection module identifies the
user's face and facial features, the analysis module analyzes the facial
features using statistical parameters and classification models, the
computation module computes the weighted image of the user, the matching
module matches the weighted image of the user with weighted advertisements
based on matching rules set by the advertisement providers, and the display
device displays the advertisement contents. The system operates in real-time
and updates the classification models continuously. The advertisement contents
are based on the user's age, gender, emotion, style, and attention and are
provided by the advertisement providers with matching rules.
#
[Scalable Verification for Safety-Critical Deep
Networks](https://arxiv.org/pdf/1801.05950.pdf)
[https://arxiv.org/pdf/1801.05950.pdf](https://arxiv.org/pdf/1801.05950.pdf)
"Verifying that neural networks behave as intended may soon become a limiting
factor in their applicability to real-world, safetycritical systems such as
those used to control autonomous vehicles safety and reliability on DNNs.
verify properties of DNNs. A major challenge of verifying properties of DNNs
with satisfiability modulo theories (SMT) solvers is in handling the networks’
activation functions such as, Reluplex (domain-specific theory solvers;
through a lazy approach). 1)devising scalable verification techniques.
2)identifying design choices -> amenable to verification. "
Each neuron of a neural network computes a weighted sum of its inputs
according to learned weights. It then passes that sum through an activation
function to produce the neuron’s final output. Typically, the activation
functions introduce nonlinearity to the network, making DNNs capable of
learning arbitrarily complex functions, but also making the job of automated
verification tools much harder.
#
Driver Assistance Systems and Vision-Based System Validates Driver Monitoring
[Vision-based convolutional neural network system detects phone usage, eating,
and
drinking.](https://www.techbriefs.com/component/content/article/tb/supplements/pit/features/technology-
leaders/36144) cameras with active infrared lighting; 30 Hz and delivered
8-bit grayscale images at 1280 × 1024-pixel resolution; ResNeXt-34;
[video-based driver assistance systems, such as automated driving;
](https://www.bosch-mobility-solutions.com/en/products-and-services/passenger-
cars-and-light-commercial-vehicles/driver-assistance-systems/lane-departure-
warning/multi-purpose-camera/)resilient object detection and tracking; camera:
± 50°field of view (horizontal); +27°/ -21°field of view (vertical); > 150 m
detection range; 2.6 MP resolution.
multi path approach:
1. classifier: for pattern recognition; resilient object detection
2. dense optical flow and structure from motion; to detect static objects; 3D structure
3. deep learning: classify objects, road, edge road, orientation;
" Operation principle of the multi purpose camera: During assisted and
automated driving, the vehicle must know what is happening in its surroundings
at all times. It must reliably detect objects and people, and be able to react
to these appropriately. Here, the latest generation of the front video camera
from Bosch plays a crucial part: The multi purpose camera for assisted and
partially automated driving utilizes an innovative, high-performance system-
on-chip (SoC) with a Bosch microprocessor for image-processing algorithms. Its
unique multipath approach combines classic image-processing algorithms with
artificial-intelligence methods for comprehensive scene interpretation and
reliable object detection. With its algorithmic multipath approach and the
innovative system-on-chip, this camera generation has been specially developed
for high-performance driver assistance systems. In line with this approach,
the multi purpose camera uses for example the following technical paths at
once for image processing: The first of these is the conventional approach
already in use today. Via preprogrammed algorithms, the cameras recognize the
typical appearance of object categories such as vehicles, cyclists, or road
markings. The second and third paths are new, however. For the second path,
the camera uses the optical flow and the structure from motion (SfM) to
recognize raised objects along the roadside, such as curbs, central reserves,
or safety barriers. The motion of associated pixels is tracked. A three-
dimensional structure is then approximated based on the two-dimensional camera
image. The third path relies on artificial intelligence. Thanks to machine-
learning processes, the camera has learned to classify objects such as cars
parked by the side of the road. The latest generation can differentiate
between surfaces on the road and those alongside the road via neuronal
networks and semantic segmentation. Additional paths are used as required:
These include classic line scanning, light detection, and stereo disparity. "
[Link](https://www.bosch-mobility-solutions.com/en/products-and-
services/passenger-cars-and-light-commercial-vehicles/driver-assistance-
systems/lane-departure-warning/multi-purpose-camera/)
Face recognition/attributes
I use citation plugin
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2. create folder "Reading notes"
3. use Ctrl+Shift+O to select reference
4. automatically create file based on that reference
5. Ctrl+Shift+E to insert link to citation page
6.
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# FQA
Scalable Verification for Safety-Critical Deep Networks
Driver Assistance Systems and Vision-Based System Validates Driver Monitoring
The present invention relates to a system for providing advertisement contents
based on facial analysis. The system consists of an image acquisition device,
a face detection module, an analysis module, a classification module, a
database, a computation module, a matching module and a display device. The
image acquisition device acquires an image of a user, the face detection
module detects the face of the user in the image, the analysis module analyses
the facial features statistically using classification models, the database
stores matching rules, weighted advertisements and a plurality of
advertisement contents and the display device displays the advertisement
contents. The computation module computes the weighted image of the user and
the matching module matches the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. The system aims to provide advertisement contents via a digital
standee by extracting salient demographic from a user to indirectly obtain
user information and behavioral preference.
The present invention relates to a system and method for providing
advertisement contents based on facial analysis using a digital standee. The
system (100) is embedded in the digital standee and comprises an image
acquisition device, a face detection module, a classification module, a data
analysis module, a computation module, a database and a matching module. The
image acquisition device is configured to acquire an image of a user, and the
face detection module uses deep learning technology to detect the user's face
in the image. The classification module classifies the user's facial features
into a plurality of classification models, such as gender, age range, emotion,
style and attention. The data analysis module obtains behavioral preference
and information of the user by analyzing the classified facial features. The
matching module matches the information with types of businesses to provide
suitable advertisement contents to the user based on rules set by the
advertisement provider. The advertisement contents are displayed on a display
device in the system.
This patent describes a system for providing advertisements based on facial
analysis. The system consists of an image acquisition device, a face detection
module, an analysis module, a computation module, a matching module, a
database, and a display device. The image acquisition device captures an image
of the user, the face detection module identifies the user's face and facial
features, the analysis module analyses the facial features using statistical
parameters and classification models, the computation module computes the
weighted image of the user, the matching module matches the weighted image of
the user with weighted advertisements, and the display device displays the
advertisement contents. The system operates in real-time and updates the
classification models continuously. The advertisement contents are based on
the user's age, gender, emotion, style, and attention and are provided by the
advertisement providers with matching rules.
The process described in this patent involves matching a user's weighted image
with a weighted advertisement based on matching rules established by the
advertisement providers. The matching rules may include order of features,
most similar features, important features, and nearest similar features. The
matching is done from left to right of the binary sequence. The selected
advertisement content is then displayed by the display device. The terms used
in the patent are defined as specified. The invention is open to changes in
form and details.
The system (100) is a device for providing advertisement contents based on
facial analysis. It consists of: an image acquisition device (10) to acquire
an image of a user, a face detection module (20) to detect the face and obtain
facial features, an analysis module (40) to analyze the facial features
statistically using classification models, a database (60) to store matching
rules and advertisements, and a display device (80) to display the selected
advertisement content. The system also has a computation module (50) to
compute a weighted image of the user based on the analyzed facial features,
and a matching module (70) to match the weighted image of the user with the
weighted advertisement to select the advertisement content. The system can
work for a single user or a group of users. The method (200) of providing
advertisement content follows similar steps as the system (100). The steps
include acquiring an image of the user, detecting the face, analyzing the
facial features, computing a weighted image of the user, obtaining matching
rules, and matching the weighted image with the weighted advertisement. The
method also includes steps of training the classification models and providing
display of the selected advertisement content.
This patent describes a system for providing advertisements based on facial
analysis using a digital standee. The system consists of an image acquisition
device, a face detection module, an analysis module, a computation module, a
matching module, a database, and a display device. The image acquisition
device captures an image of the user, the face detection module identifies the
user's face and facial features, the analysis module analyzes the facial
features using statistical parameters and classification models, the
computation module computes the weighted image of the user, the matching
module matches the weighted image of the user with weighted advertisements
based on matching rules set by the advertisement providers, and the display
device displays the advertisement contents. The system operates in real-time
and updates the classification models continuously. The advertisement contents
are based on the user's age, gender, emotion, style, and attention and are
provided by the advertisement providers with matching rules.
#
[Scalable Verification for Safety-Critical Deep
Networks](https://arxiv.org/pdf/1801.05950.pdf)
[https://arxiv.org/pdf/1801.05950.pdf](https://arxiv.org/pdf/1801.05950.pdf)
"Verifying that neural networks behave as intended may soon become a limiting
factor in their applicability to real-world, safetycritical systems such as
those used to control autonomous vehicles safety and reliability on DNNs.
verify properties of DNNs. A major challenge of verifying properties of DNNs
with satisfiability modulo theories (SMT) solvers is in handling the networks’
activation functions such as, Reluplex (domain-specific theory solvers;
through a lazy approach). 1)devising scalable verification techniques.
2)identifying design choices -> amenable to verification. "
Each neuron of a neural network computes a weighted sum of its inputs
according to learned weights. It then passes that sum through an activation
function to produce the neuron’s final output. Typically, the activation
functions introduce nonlinearity to the network, making DNNs capable of
learning arbitrarily complex functions, but also making the job of automated
verification tools much harder.
#
Driver Assistance Systems and Vision-Based System Validates Driver Monitoring
[Vision-based convolutional neural network system detects phone usage, eating,
and
drinking.](https://www.techbriefs.com/component/content/article/tb/supplements/pit/features/technology-
leaders/36144) cameras with active infrared lighting; 30 Hz and delivered
8-bit grayscale images at 1280 × 1024-pixel resolution; ResNeXt-34;
[video-based driver assistance systems, such as automated driving;
](https://www.bosch-mobility-solutions.com/en/products-and-services/passenger-
cars-and-light-commercial-vehicles/driver-assistance-systems/lane-departure-
warning/multi-purpose-camera/)resilient object detection and tracking; camera:
± 50°field of view (horizontal); +27°/ -21°field of view (vertical); > 150 m
detection range; 2.6 MP resolution.
multi path approach:
1. classifier: for pattern recognition; resilient object detection
2. dense optical flow and structure from motion; to detect static objects; 3D structure
3. deep learning: classify objects, road, edge road, orientation;
" Operation principle of the multi purpose camera: During assisted and
automated driving, the vehicle must know what is happening in its surroundings
at all times. It must reliably detect objects and people, and be able to react
to these appropriately. Here, the latest generation of the front video camera
from Bosch plays a crucial part: The multi purpose camera for assisted and
partially automated driving utilizes an innovative, high-performance system-
on-chip (SoC) with a Bosch microprocessor for image-processing algorithms. Its
unique multipath approach combines classic image-processing algorithms with
artificial-intelligence methods for comprehensive scene interpretation and
reliable object detection. With its algorithmic multipath approach and the
innovative system-on-chip, this camera generation has been specially developed
for high-performance driver assistance systems. In line with this approach,
the multi purpose camera uses for example the following technical paths at
once for image processing: The first of these is the conventional approach
already in use today. Via preprogrammed algorithms, the cameras recognize the
typical appearance of object categories such as vehicles, cyclists, or road
markings. The second and third paths are new, however. For the second path,
the camera uses the optical flow and the structure from motion (SfM) to
recognize raised objects along the roadside, such as curbs, central reserves,
or safety barriers. The motion of associated pixels is tracked. A three-
dimensional structure is then approximated based on the two-dimensional camera
image. The third path relies on artificial intelligence. Thanks to machine-
learning processes, the camera has learned to classify objects such as cars
parked by the side of the road. The latest generation can differentiate
between surfaces on the road and those alongside the road via neuronal
networks and semantic segmentation. Additional paths are used as required:
These include classic line scanning, light detection, and stereo disparity. "
[Link](https://www.bosch-mobility-solutions.com/en/products-and-
services/passenger-cars-and-light-commercial-vehicles/driver-assistance-
systems/lane-departure-warning/multi-purpose-camera/)
Face recognition/attributes
I use citation plugin
1. add path to the JabRef database "reading notes/dh.bib"
2. create folder "Reading notes"
3. use Ctrl+Shift+O to select reference
4. automatically create file based on that reference
5. Ctrl+Shift+E to insert link to citation page
6.
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
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Navigator](https://www.linkedin.com/sales/ssi?src=or-
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[Computer
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# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
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* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
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* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
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* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
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* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[](/home)[Computer
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* [Machine Learning Specialization](/courses/machine-learning-specialization)
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* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
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* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
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* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
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* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
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* [فارسی](/topics-and-projects/فارسی)
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* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
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[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
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* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
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* [Workshops and Events](/workshops-and-events)
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* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
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* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
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* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
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* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
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[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
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[Computer
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# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
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* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
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* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
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* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
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* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[](/home)[Computer
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* [Machine Learning Specialization](/courses/machine-learning-specialization)
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* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
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* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
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* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
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* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
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* [فارسی](/topics-and-projects/فارسی)
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* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
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[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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using this site, you agree to its use of cookies.
[](/home)[Computer Vision, Deep Learning,
Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep Learning, Artificial
superintelligence (ASI)](/home)
# Links
pirahansiah, Personal, Roadmap, Books, Online Courses
[](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze-
oXQexwY "Open Spreadsheet, Links in new window")
Links
Links from my research and interest
AI in RISC-V
Mixed reality
Old Links
Reference
Link Groups
Top website
Python
Computer Vision
Documents
Business
Tools
Patents
Books
Journal Papers
Conference Papers
Articles on LinkedIn
Slides slideshare
Online Courses
Learning path
Completed
Reference
Link of best Groups
Books
Videos
Papers
Site
Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning
training loops.
GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp)
[https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to-
install-remmina/)
[https://readme.so/](https://readme.so/)
#
Links from my research and interest
[Build Better Generative Adversarial Networks (GANs) - Andrew Ng -
2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_-
VnF)
[https://www.oreilly.com/library/view/flow-
architectures/9781492075882/](https://www.oreilly.com/library/view/flow-
architectures/9781492075882/)
##
AI in RISC-V
* [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020)
##
Mixed reality
* [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera)
* Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.
#
Old Links
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Top website
* [https://paperswithcode.com/](https://paperswithcode.com/)
* [https://www.learnthepart.com/](https://www.learnthepart.com/)
* [https://hackaday.com/](https://hackaday.com/)
* [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762)
* hbrew install youtube-dl
* [https://www.masterclass.com/](https://www.masterclass.com/)
* [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp)
* [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona)
* [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
* [https://explainshell.com/](https://explainshell.com/)
* [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20)
* [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4)
* [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4)
###
Python
* [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/)
* [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/)
* [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594)
###
Computer Vision
* [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/)
* [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md)
* [https://github.com/google/mediapipe](https://github.com/google/mediapipe)
###
Documents
* [https://book.keybase.io/docs](https://book.keybase.io/docs)
* [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind)
* [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/)
* [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/)
* [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/)
* [https://kepler.gl/demo](https://kepler.gl/demo)
* [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/)
* [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE)
###
Business
* [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006)
* [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5)
###
Tools
* Free convert video for mac opensource
* [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake)
* [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg)
* [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c)
* [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises
* [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap
* [Create and share beautiful images of your source code.](https://carbon.now.sh/)
* [Web IDE](https://replit.com/templates)
* [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2)
###
Patents
[https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
2. A method for augmenting a plurality of face images (WO2021060971A1)
3. A method for detecting a moving vehicle (WO2021107761A1)
1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2)
The present invention relates to a system (100) for providing advertisement
contents based on facial analysis comprising an image acquisition device (10)
to acquire an image of a user, a face detection module (20) to detect face of
the user in the image, an analysis module (40) to analyse the facial features
statistically using classification models retrieved from a classification
module (30), a database (60) to store matching rules, weighted advertisements
and a plurality of advertisement contents; and a display device (80) to
display the advertisement contents. The system (100) further comprises a
computation module (50) to compute weighted image of the user and a matching
module (70) to match the weighted image of the user with the weighted
advertisement to select an advertisement content based on facial analysis of
the user. A method of providing the advertisement contents based on facial
analysis is also provided thereof.
[https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf)
2. A method for augmenting a plurality of face images (WO2021060971A1)
The present invention relates to a method for increasing data for face
analysis in video surveillance. The method comprises the steps of acquiring at
least one face image from an image acquisition module (102), acquiring a
plurality of face images available on the internet using a data input module
(104), increasing face images by at least one data augmentation module (106
and 107), generating a plurality of face images based on a trained Generative
Adversarial Network, GAN technique by using a GAN module, selecting proper
images based on quality of the face images using a fuzzy logic module (111),
saving the selected images into a fifth database, and training the deep
learning module (113).
[https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf)
3. A method for detecting a moving vehicle (WO2021107761A1)
The present invention relates to a method for detecting a moving vehicle. The
method comprises the steps of grabbing an initial image from a video stream by
a vehicle detection module (1100), wherein the vehicle detection module (1100)
is a part of a system (1000) to identify moving vehicle, enhancing the
illumination of the initial image by the vehicle detection module (1100),
enhancing the edges within the initial image by the vehicle detection module
(1100), and finding vehicle based on homogenous properties of the body of the
vehicle by the vehicle detection module (1100). The step of finding vehicle
based on homogenous property of the body of the vehicle by the vehicle
detection module (1100) further comprising the sub-steps of closing open
edges, inverting the binary image, segmenting an inverted binary image,
filtering the noise based on geometric feature, and filtering the noise based
on relation.
[https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf)
###
Books
1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394)
2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/)
###
Journal Papers
* Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21)
* * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf)
* GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6)
* * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf)
* Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9).
* * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM)
* * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf)
* Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2)
* * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf)
* Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52
* * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf)
###
Conference Papers
* Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017)
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342)
* Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015)
* [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf)
* Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015)
* [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336)
* 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012)
* [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf)
* Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics
* [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918)
* License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011)
* [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627)
* Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011)
* [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf)
* [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649)
* Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85.
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532)
* Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010)
* [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125)
* [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf)
* Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050
* [http://waset.org/publications/3636](http://waset.org/publications/3636)
* An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)
* [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en)
###
Articles on LinkedIn
* [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/))
* [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) )
* [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/))
* [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/)
* [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/)
###
Slides slideshare
* Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation)
* Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) )
* tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) )
* Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) )
* Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) )
* Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) )
* Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) )
* How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) )
* Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) )
* Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) )
###
Online Courses
* [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact)
* [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life)
* [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2)
* [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends)
* [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business)
* [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity)
* [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2)
* [Understanding Business ](https://www.linkedin.com/learning/understanding-business)
* [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea)
* [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship)
* [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations)
* [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations)
* [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations)
* [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips)
* [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation)
* [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments)
* [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow)
* [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families)
* [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances)
* [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2)
* [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2)
* [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv)
* [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers)
* [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea)
* [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital)
* [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving)
* [Professional Networking ](https://www.linkedin.com/learning/professional-networking)
* [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home)
* [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital)
* [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling)
* [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence)
* [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers)
###
Learning path
* [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance)
* [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner)
#
Completed
###
Reference
[https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing)
###
Link of best Groups
* [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/)
* [Source code](https://gitlab.com/pirahansiah)
* [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/)
* [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5)
* [GitHub](https://github.com/pirahansiah)
###
Books
* [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y)
* [Mathematics for Machine Learning](https://mml-book.github.io/)
* Principles of Economics (6th edition)
* The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It
* Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)
* Reinventing Your Life: The Breakthough Program to End Negative Behavior
* Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence
* [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf)
* * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)
* [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/)
* [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
* Multiple view geometry in computer vision
* Learning OpenCV Book by Adrian Kaehler and Gary Bradski
* Digital Image Processing, Global Edition by Rafael C. Gonzalez
* Introduction to Algorithms, 3rd Edition (The MIT Press)
* The Mythical Man-Month: Essays on Software Engineering
* [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition)
###
Videos
* [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning)
* [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
* [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/)
* [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY)
###
Papers
* CALTag: High Precision Fiducial Markers for Camera
* Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
* Analysis of focus measure operators in shape-from-focus
* Optical flow modeling and computation: A survey
* Toward general type 2 fuzzy logic systems based on zSlices
###
Site
* [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#))
Reference-
ComputerVisionDeepLearning.pdf
* [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w)
* [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679)
* [https://www.freertos.org/index.html](https://www.freertos.org/index.html)
* [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation)
* [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/)
* [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
* [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917)
* [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf)
* [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/)
* [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833)
* [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238)
*
[https://koinly.io/](https://koinly.io/)
site map: octopus.do
[https://www.startupgermany.nrw/startup-
contest/](https://www.startupgermany.nrw/startup-contest/)
[https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth-
camera-4k-cv-edge-object-
detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai-
kit-oak-depth-camera-4k-cv-edge-object-detection/description)
[HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube
in 2021! - YouTube
](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z)
[https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg)
[محمد رضا شجریان، آلبوم کامل در خیال -
YouTube](https://www.youtube.com/watch?v=cFdApmqlllg)
[دوره جامع گوگل آنالیتیکس(UA) |
آنالیتیپس](https://analytips.io/product/google-analyticsua/)
[رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا -
YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc)
[Social Selling Index | Sales
Navigator](https://www.linkedin.com/sales/ssi?src=or-
search&veh=www.google.com)
[https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac-
os-x-3b2d4c4a4827)
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* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# startup
Accelerators and Incubators - These are programs that provide funding,
mentorship, and resources to help startups grow and scale.
* Startup Labs
* Launchpads
* Innovation Centers
* Venture Studios
* Seed Funds
* Innovation Hubs
* Entrepreneurship Centers
* Co-creation Spaces
* Innovation Workshops
* Startup Communities
Germany:
* Top
*
* Other
* Rocket Internet
* Wayra - A startup accelerator backed by Telefónica that provides funding, mentorship, and access to a global network of investors.
* Axel Springer Plug and Play - A startup accelerator focused on media, advertising, and digital content.
* High-Tech Gründerfonds - A seed fund that invests in technology startups in various sectors, including software, hardware, and engineering.
* Berlin Startup Academy - A startup accelerator that offers a 3-month program of mentorship, workshops, and networking opportunities.
* Startupbootcamp Berlin - A startup accelerator that offers a 3-month program focused on fintech, e-commerce, and smart transportation.
* Factory Berlin - A co-working and innovation hub that provides resources and support for startups in various industries.
* Founders Factory - A startup accelerator and incubator that offers mentorship, funding, and access to a global network of investors.
* Next Big Thing AG - A startup incubator that focuses on the Internet of Things (IoT) and connected devices.
* German Tech Entrepreneurship Center (GTEC) - A startup incubator and accelerator that offers programs and resources for entrepreneurs in various sectors, including fintech, healthtech, and mobility.
* Factory Berlin: Factory Berlin is a co-working space that provides startups and entrepreneurs with access to a supportive community, events, and resources.
* Berlin Startup Incubator: Berlin Startup Incubator offers startups in the technology sector access to mentorship, funding, and office space.
* Betahaus: Betahaus is a co-working space that offers startups and entrepreneurs access to a community of like-minded individuals, events, and resources.
* The Family: The Family is a startup accelerator that offers entrepreneurs mentorship, funding, and resources to help them build successful businesses.
Venture Capital Firms - These firms provide funding and support to startups in
exchange for equity in the company.
Co-Working Spaces - These spaces provide a physical location for startups to
work and collaborate with other entrepreneurs.
Startup Consulting Firms - These firms provide guidance and advice to startups
on a range of topics, such as business strategy, marketing, and operations.
Business Plan Writers - These professionals can help startups create a
comprehensive business plan that outlines their goals, strategies, and
financial projections.
##
Profit:
Accelerators and incubators can earn money in a variety of ways, depending on
their business model. Some common revenue streams for accelerators and
incubators include:
* Sponsorship - Many accelerators and incubators are sponsored by corporations, foundations, or government agencies, who provide funding in exchange for branding, marketing, or other benefits.
* Equity Investment - Some accelerators and incubators do take equity in the startups they support, which allows them to earn a return on their investment if the startup is successful.
* Program Fees - Some accelerators and incubators charge startups a fee to participate in their programs, which may include access to mentorship, resources, or networking opportunities.
* Consulting or Advisory Services - Some accelerators and incubators may offer consulting or advisory services to startups for a fee.
* Event or Conference Revenue - Some accelerators and incubators may host events or conferences, which can generate revenue through ticket sales, sponsorships, or exhibitor fees.
##
grant / loan
A grant or a loan can be an attractive option for startups that are looking
for funding but do not want to give up equity in their company.
A grant is a sum of money that is given to a startup with no obligation to pay
it back. Grants are often offered by government agencies, non-profit
organizations, or foundations that want to support innovation and
entrepreneurship in a particular sector or industry. Grants can be highly
competitive, and startups typically have to submit a detailed proposal
outlining their business plan, goals, and how they plan to use the funds.
A loan, on the other hand, is a sum of money that is borrowed from a lender
with the obligation to pay it back over a specified period of time, usually
with interest. Loans can be offered by banks, government agencies, or private
investors. Startups typically have to submit a detailed business plan and
financial projections to qualify for a loan. Loans can be a good option for
startups that have a solid plan for revenue generation but need some initial
capital to get started.
Both grants and loans can provide startups with much-needed funding to help
them get off the ground. However, it's important to carefully consider the
terms and conditions of any funding agreement before accepting it. Startups
should make sure that they understand the repayment terms, interest rates, and
any other fees or requirements associated with the grant or loan.
sole proprietorship (Einzelunternehmen)
Investor Database
0\. fundraising content
1\. alternative datases
accelerator/ incubator
business angels
competitions / conferences
investors
investor matching / public funding
startups
2\. alternative funding options
crowd investments europe
family offices europepubluic funding germany
3\. programs
accelerator / incubator DACH-Region
Company builder DACH-Region
innovation labs DACH-Region
4\. business angels
business angels germany
business angels europe
carta
5\. VCs
corporate venture capital europe
venture capital europe
US venture capital invested in europe
6\. Networks
coaching & mentoring
different industries / verticals
female founder / diversity
founderinvestor
investor reviews
7\. specials
company setup agencies
dealflow agencies europe
fundraising agencies DACH-Region
venture capital law firms germany
Accelerators and Incubators - These are programs that provide funding,
mentorship, and resources to help startups grow and scale.
Venture Capital Firms - These firms provide funding and support to startups in
exchange for equity in the company.
Co-Working Spaces - These spaces provide a physical location for startups to
work and collaborate with other entrepreneurs.
Startup Consulting Firms - These firms provide guidance and advice to startups
on a range of topics, such as business strategy, marketing, and operations.
Business Plan Writers - These professionals can help startups create a
comprehensive business plan that outlines their goals, strategies, and
financial projections.
Accelerators and Incubators - These are programs that provide funding,
mentorship, and resources to help startups grow and scale.
Venture Capital Firms - These firms provide funding and support to startups in
exchange for equity in the company.
Co-Working Spaces - These spaces provide a physical location for startups to
work and collaborate with other entrepreneurs.
Startup Consulting Firms - These firms provide guidance and advice to startups
on a range of topics, such as business strategy, marketing, and operations.
Business Plan Writers - These professionals can help startups create a
comprehensive business plan that outlines their goals, strategies, and
financial projections.
Accelerators and Incubators - These are programs that provide funding,
mentorship, and resources to help startups grow and scale.
Venture Capital Firms - These firms provide funding and support to startups in
exchange for equity in the company.
Co-Working Spaces - These spaces provide a physical location for startups to
work and collaborate with other entrepreneurs.
Startup Consulting Firms - These firms provide guidance and advice to startups
on a range of topics, such as business strategy, marketing, and operations.
Business Plan Writers - These professionals can help startups create a
comprehensive business plan that outlines their goals, strategies, and
financial projections.
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# startup
Accelerators and Incubators - These are programs that provide funding,
mentorship, and resources to help startups grow and scale.
* Startup Labs
* Launchpads
* Innovation Centers
* Venture Studios
* Seed Funds
* Innovation Hubs
* Entrepreneurship Centers
* Co-creation Spaces
* Innovation Workshops
* Startup Communities
Germany:
* Top
*
* Other
* Rocket Internet
* Wayra - A startup accelerator backed by Telefónica that provides funding, mentorship, and access to a global network of investors.
* Axel Springer Plug and Play - A startup accelerator focused on media, advertising, and digital content.
* High-Tech Gründerfonds - A seed fund that invests in technology startups in various sectors, including software, hardware, and engineering.
* Berlin Startup Academy - A startup accelerator that offers a 3-month program of mentorship, workshops, and networking opportunities.
* Startupbootcamp Berlin - A startup accelerator that offers a 3-month program focused on fintech, e-commerce, and smart transportation.
* Factory Berlin - A co-working and innovation hub that provides resources and support for startups in various industries.
* Founders Factory - A startup accelerator and incubator that offers mentorship, funding, and access to a global network of investors.
* Next Big Thing AG - A startup incubator that focuses on the Internet of Things (IoT) and connected devices.
* German Tech Entrepreneurship Center (GTEC) - A startup incubator and accelerator that offers programs and resources for entrepreneurs in various sectors, including fintech, healthtech, and mobility.
* Factory Berlin: Factory Berlin is a co-working space that provides startups and entrepreneurs with access to a supportive community, events, and resources.
* Berlin Startup Incubator: Berlin Startup Incubator offers startups in the technology sector access to mentorship, funding, and office space.
* Betahaus: Betahaus is a co-working space that offers startups and entrepreneurs access to a community of like-minded individuals, events, and resources.
* The Family: The Family is a startup accelerator that offers entrepreneurs mentorship, funding, and resources to help them build successful businesses.
Venture Capital Firms - These firms provide funding and support to startups in
exchange for equity in the company.
Co-Working Spaces - These spaces provide a physical location for startups to
work and collaborate with other entrepreneurs.
Startup Consulting Firms - These firms provide guidance and advice to startups
on a range of topics, such as business strategy, marketing, and operations.
Business Plan Writers - These professionals can help startups create a
comprehensive business plan that outlines their goals, strategies, and
financial projections.
##
Profit:
Accelerators and incubators can earn money in a variety of ways, depending on
their business model. Some common revenue streams for accelerators and
incubators include:
* Sponsorship - Many accelerators and incubators are sponsored by corporations, foundations, or government agencies, who provide funding in exchange for branding, marketing, or other benefits.
* Equity Investment - Some accelerators and incubators do take equity in the startups they support, which allows them to earn a return on their investment if the startup is successful.
* Program Fees - Some accelerators and incubators charge startups a fee to participate in their programs, which may include access to mentorship, resources, or networking opportunities.
* Consulting or Advisory Services - Some accelerators and incubators may offer consulting or advisory services to startups for a fee.
* Event or Conference Revenue - Some accelerators and incubators may host events or conferences, which can generate revenue through ticket sales, sponsorships, or exhibitor fees.
##
grant / loan
A grant or a loan can be an attractive option for startups that are looking
for funding but do not want to give up equity in their company.
A grant is a sum of money that is given to a startup with no obligation to pay
it back. Grants are often offered by government agencies, non-profit
organizations, or foundations that want to support innovation and
entrepreneurship in a particular sector or industry. Grants can be highly
competitive, and startups typically have to submit a detailed proposal
outlining their business plan, goals, and how they plan to use the funds.
A loan, on the other hand, is a sum of money that is borrowed from a lender
with the obligation to pay it back over a specified period of time, usually
with interest. Loans can be offered by banks, government agencies, or private
investors. Startups typically have to submit a detailed business plan and
financial projections to qualify for a loan. Loans can be a good option for
startups that have a solid plan for revenue generation but need some initial
capital to get started.
Both grants and loans can provide startups with much-needed funding to help
them get off the ground. However, it's important to carefully consider the
terms and conditions of any funding agreement before accepting it. Startups
should make sure that they understand the repayment terms, interest rates, and
any other fees or requirements associated with the grant or loan.
sole proprietorship (Einzelunternehmen)
Investor Database
0\. fundraising content
1\. alternative datases
accelerator/ incubator
business angels
competitions / conferences
investors
investor matching / public funding
startups
2\. alternative funding options
crowd investments europe
family offices europepubluic funding germany
3\. programs
accelerator / incubator DACH-Region
Company builder DACH-Region
innovation labs DACH-Region
4\. business angels
business angels germany
business angels europe
carta
5\. VCs
corporate venture capital europe
venture capital europe
US venture capital invested in europe
6\. Networks
coaching & mentoring
different industries / verticals
female founder / diversity
founderinvestor
investor reviews
7\. specials
company setup agencies
dealflow agencies europe
fundraising agencies DACH-Region
venture capital law firms germany
Accelerators and Incubators - These are programs that provide funding,
mentorship, and resources to help startups grow and scale.
Venture Capital Firms - These firms provide funding and support to startups in
exchange for equity in the company.
Co-Working Spaces - These spaces provide a physical location for startups to
work and collaborate with other entrepreneurs.
Startup Consulting Firms - These firms provide guidance and advice to startups
on a range of topics, such as business strategy, marketing, and operations.
Business Plan Writers - These professionals can help startups create a
comprehensive business plan that outlines their goals, strategies, and
financial projections.
Accelerators and Incubators - These are programs that provide funding,
mentorship, and resources to help startups grow and scale.
Venture Capital Firms - These firms provide funding and support to startups in
exchange for equity in the company.
Co-Working Spaces - These spaces provide a physical location for startups to
work and collaborate with other entrepreneurs.
Startup Consulting Firms - These firms provide guidance and advice to startups
on a range of topics, such as business strategy, marketing, and operations.
Business Plan Writers - These professionals can help startups create a
comprehensive business plan that outlines their goals, strategies, and
financial projections.
Accelerators and Incubators - These are programs that provide funding,
mentorship, and resources to help startups grow and scale.
Venture Capital Firms - These firms provide funding and support to startups in
exchange for equity in the company.
Co-Working Spaces - These spaces provide a physical location for startups to
work and collaborate with other entrepreneurs.
Startup Consulting Firms - These firms provide guidance and advice to startups
on a range of topics, such as business strategy, marketing, and operations.
Business Plan Writers - These professionals can help startups create a
comprehensive business plan that outlines their goals, strategies, and
financial projections.
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer Vision, Deep Learning,
Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep Learning, Artificial
superintelligence (ASI)](/home)
# startup
Accelerators and Incubators - These are programs that provide funding,
mentorship, and resources to help startups grow and scale.
* Startup Labs
* Launchpads
* Innovation Centers
* Venture Studios
* Seed Funds
* Innovation Hubs
* Entrepreneurship Centers
* Co-creation Spaces
* Innovation Workshops
* Startup Communities
Germany:
* Top
*
* Other
* Rocket Internet
* Wayra - A startup accelerator backed by Telefónica that provides funding, mentorship, and access to a global network of investors.
* Axel Springer Plug and Play - A startup accelerator focused on media, advertising, and digital content.
* High-Tech Gründerfonds - A seed fund that invests in technology startups in various sectors, including software, hardware, and engineering.
* Berlin Startup Academy - A startup accelerator that offers a 3-month program of mentorship, workshops, and networking opportunities.
* Startupbootcamp Berlin - A startup accelerator that offers a 3-month program focused on fintech, e-commerce, and smart transportation.
* Factory Berlin - A co-working and innovation hub that provides resources and support for startups in various industries.
* Founders Factory - A startup accelerator and incubator that offers mentorship, funding, and access to a global network of investors.
* Next Big Thing AG - A startup incubator that focuses on the Internet of Things (IoT) and connected devices.
* German Tech Entrepreneurship Center (GTEC) - A startup incubator and accelerator that offers programs and resources for entrepreneurs in various sectors, including fintech, healthtech, and mobility.
* Factory Berlin: Factory Berlin is a co-working space that provides startups and entrepreneurs with access to a supportive community, events, and resources.
* Berlin Startup Incubator: Berlin Startup Incubator offers startups in the technology sector access to mentorship, funding, and office space.
* Betahaus: Betahaus is a co-working space that offers startups and entrepreneurs access to a community of like-minded individuals, events, and resources.
* The Family: The Family is a startup accelerator that offers entrepreneurs mentorship, funding, and resources to help them build successful businesses.
Venture Capital Firms - These firms provide funding and support to startups in
exchange for equity in the company.
Co-Working Spaces - These spaces provide a physical location for startups to
work and collaborate with other entrepreneurs.
Startup Consulting Firms - These firms provide guidance and advice to startups
on a range of topics, such as business strategy, marketing, and operations.
Business Plan Writers - These professionals can help startups create a
comprehensive business plan that outlines their goals, strategies, and
financial projections.
##
Profit:
Accelerators and incubators can earn money in a variety of ways, depending on
their business model. Some common revenue streams for accelerators and
incubators include:
* Sponsorship - Many accelerators and incubators are sponsored by corporations, foundations, or government agencies, who provide funding in exchange for branding, marketing, or other benefits.
* Equity Investment - Some accelerators and incubators do take equity in the startups they support, which allows them to earn a return on their investment if the startup is successful.
* Program Fees - Some accelerators and incubators charge startups a fee to participate in their programs, which may include access to mentorship, resources, or networking opportunities.
* Consulting or Advisory Services - Some accelerators and incubators may offer consulting or advisory services to startups for a fee.
* Event or Conference Revenue - Some accelerators and incubators may host events or conferences, which can generate revenue through ticket sales, sponsorships, or exhibitor fees.
##
grant / loan
A grant or a loan can be an attractive option for startups that are looking
for funding but do not want to give up equity in their company.
A grant is a sum of money that is given to a startup with no obligation to pay
it back. Grants are often offered by government agencies, non-profit
organizations, or foundations that want to support innovation and
entrepreneurship in a particular sector or industry. Grants can be highly
competitive, and startups typically have to submit a detailed proposal
outlining their business plan, goals, and how they plan to use the funds.
A loan, on the other hand, is a sum of money that is borrowed from a lender
with the obligation to pay it back over a specified period of time, usually
with interest. Loans can be offered by banks, government agencies, or private
investors. Startups typically have to submit a detailed business plan and
financial projections to qualify for a loan. Loans can be a good option for
startups that have a solid plan for revenue generation but need some initial
capital to get started.
Both grants and loans can provide startups with much-needed funding to help
them get off the ground. However, it's important to carefully consider the
terms and conditions of any funding agreement before accepting it. Startups
should make sure that they understand the repayment terms, interest rates, and
any other fees or requirements associated with the grant or loan.
sole proprietorship (Einzelunternehmen)
Investor Database
0\. fundraising content
1\. alternative datases
accelerator/ incubator
business angels
competitions / conferences
investors
investor matching / public funding
startups
2\. alternative funding options
crowd investments europe
family offices europepubluic funding germany
3\. programs
accelerator / incubator DACH-Region
Company builder DACH-Region
innovation labs DACH-Region
4\. business angels
business angels germany
business angels europe
carta
5\. VCs
corporate venture capital europe
venture capital europe
US venture capital invested in europe
6\. Networks
coaching & mentoring
different industries / verticals
female founder / diversity
founderinvestor
investor reviews
7\. specials
company setup agencies
dealflow agencies europe
fundraising agencies DACH-Region
venture capital law firms germany
Accelerators and Incubators - These are programs that provide funding,
mentorship, and resources to help startups grow and scale.
Venture Capital Firms - These firms provide funding and support to startups in
exchange for equity in the company.
Co-Working Spaces - These spaces provide a physical location for startups to
work and collaborate with other entrepreneurs.
Startup Consulting Firms - These firms provide guidance and advice to startups
on a range of topics, such as business strategy, marketing, and operations.
Business Plan Writers - These professionals can help startups create a
comprehensive business plan that outlines their goals, strategies, and
financial projections.
Accelerators and Incubators - These are programs that provide funding,
mentorship, and resources to help startups grow and scale.
Venture Capital Firms - These firms provide funding and support to startups in
exchange for equity in the company.
Co-Working Spaces - These spaces provide a physical location for startups to
work and collaborate with other entrepreneurs.
Startup Consulting Firms - These firms provide guidance and advice to startups
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Business Plan Writers - These professionals can help startups create a
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Co-Working Spaces - These spaces provide a physical location for startups to
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Startup Consulting Firms - These firms provide guidance and advice to startups
on a range of topics, such as business strategy, marketing, and operations.
Business Plan Writers - These professionals can help startups create a
comprehensive business plan that outlines their goals, strategies, and
financial projections.
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[](/home)[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
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* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# Apple
##
iPhone 6
**1809 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
9:24h \- 10:29h
##
iPhone XS Max
**3174 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
11:06h - 13:43h
##
iPhone 14 Pro Max
**4323 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
23:39h \- 24:38h
[https://www.gsmarena.com/battery-
test.php3](https://www.google.com/url?q=https%3A%2F%2Fwww.gsmarena.com%2Fbattery-
test.php3&sa=D&sntz=1&usg=AOvVaw0osU7PANuz6t3uCn9hz13z)
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# Apple
##
iPhone 6
**1809 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
9:24h \- 10:29h
##
iPhone XS Max
**3174 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
11:06h - 13:43h
##
iPhone 14 Pro Max
**4323 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
23:39h \- 24:38h
[https://www.gsmarena.com/battery-
test.php3](https://www.google.com/url?q=https%3A%2F%2Fwww.gsmarena.com%2Fbattery-
test.php3&sa=D&sntz=1&usg=AOvVaw0osU7PANuz6t3uCn9hz13z)
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# Apple
##
iPhone 6
**1809 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
9:24h \- 10:29h
##
iPhone XS Max
**3174 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
11:06h - 13:43h
##
iPhone 14 Pro Max
**4323 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
23:39h \- 24:38h
[https://www.gsmarena.com/battery-
test.php3](https://www.google.com/url?q=https%3A%2F%2Fwww.gsmarena.com%2Fbattery-
test.php3&sa=D&sntz=1&usg=AOvVaw0osU7PANuz6t3uCn9hz13z)
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer Vision, Deep Learning,
Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep Learning, Artificial
superintelligence (ASI)](/home)
# Apple
##
iPhone 6
**1809 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
9:24h \- 10:29h
##
iPhone XS Max
**3174 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
11:06h - 13:43h
##
iPhone 14 Pro Max
**4323 mAh**
**VIDEO STREAMING.[WEB
BROWSING](https://www.google.com/url?q=https%3A%2F%2Fwww.phonearena.com%2Fnews%2FPhoneArena-
Battery-Test-
Results_id124954%23&sa=D&sntz=1&usg=AOvVaw2CR64iW7Grp8pLYoytTs-R)**
23:39h \- 24:38h
[https://www.gsmarena.com/battery-
test.php3](https://www.google.com/url?q=https%3A%2F%2Fwww.gsmarena.com%2Fbattery-
test.php3&sa=D&sntz=1&usg=AOvVaw0osU7PANuz6t3uCn9hz13z)
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer Vision, Deep Learning,
Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep Learning, Artificial
superintelligence (ASI)](/home)
# فارسی
بعد از خواندن تعداد زیادی کتاب و مقاله و ویدیوهای آموزشی که لیستشان در انتهای
این متن آمده است مطالب زیر را از تجربیاتم نوشتم
\- مهمترین قسمت نوشتن است. باید همیشه به هر صورتی که راحتید امکانات نوشتن هر
چیزی برایتان فراهم باشد. کاغذ و خودکار و یا نوت برداری در گوشی موبایل و غیره
\- نگران طبقه بندی و مرتب کردن و موضوعات مرتبط به هم نباشید . در اینجا ما روش
پایین به بالا را داریم. به اینها یادداشتهای زودگذر می گویند و حداکثر بعد از 2
روز از بین می روند. حتما منابع مورد استفاده در ایجاد این یادداشتها را باید در
انتها یا پشت کاغذ بنویسید. (می توانید اگر کتاب یا مقاله هست از برنامه های
رایگانی که رفرنسها و منابع را مدیریت می کند استفاده کنید و اینجا لینک یا کلید
اصل اون منبع را بیاورید)
\- در انتهای هر روز و یا شروع روز بعد باید تمامی یادداشتهای زودگذر نوشته شده
را بررسی کنید. در این قسمت ایده و نظر و فهم خودتان را از یادداشتها می نویسید.
هر یادداشت فقط یک ایده یا نظر باید باشد با حداقل جملات (کمتر از یک پاراگراف).
به این یادداشتهای اصلی می گویند و برای همیشه می مانند.
\- طبقه بندی: مهمترین قسمت و اصلی ترین کاری که باید انجام بدهید در این قسمت
است. ممکن است خواندن یک مقاله و نوت برداری از آن یک روز طول بکشد و مرتب کردن و
ارتباط پیدا کردن یادداشتها هم یک روز طول بکشد و حتی بیشتر از زمانی که برای
خواندن می گذارید ولی در نهایت این بخش از کار است که نتیجه اش را در آینده
خواهید دید.
\- در ابتدا باید بگردید در تمامی عناوین اصلی که دارید و مرتبط ترین را پیدا
کنید. مثلا متن شما مربوط به مدیریت دانش شخصی است و یکی از عنوانهایی که در جعبه
های یادداشتهایتان دارید مدیریت دانش است پس این متن می تواند به این عنوان مربوط
باشد
\- در مرحله بعدی نقشه محتوا مربوط به آن عنوان را بررسی کنید. ( در جلوتر توضیح
می دم که چی هست) و ممکن است چند مرحله نیاز باشد این کار را انجام دهید
\- در نقشه محتوای آخری که نزدیکترین به این یادداشت هست بگردید و جایگاه مناسب
برای قراردادن این نوت را پیدا کنید. سپس آن مجموعه یاداداشتهای مرتبط را بخوانید
و نزدیکترین موضوع مرتبط را پیدا کنید.
\- حالا نوبت به شماره دهی به یادداشتان می رسد. به یادداشت قبلی و بعدی نگان
کنید. اگر این یادداشت آخری هست شماره بعد را بنویسید و در جایگاهش بگذارید. مثلا
یادداشت قبلی 1234 بوده است و این یادداشت قرار است در انتها قرار بگیرد پس شماره
این یادداشت می شود 1235.
\- اگر این یادداشت بین دوتا یادداشت قرار گرفت شماره گذاری با اضافه کردن حروف
صورت می گیرد. مثلا بین 1234 و 1235 می شود 1234a1
\- حالا شماره این یادداشت و عنوانش ( یا کلمات کلیدی که دارد) را در انتهای نقشه
محتوا وارد کنید
\- به زودی خواهید دید که برای هر موضوعی که مطالات زیادی داشته اید کلی ایده و
نظریات و محتوای آماده استفاده داید
\- در مرحله بعدی به یادداشتهای دائمی می رسیم - بیشتر مربوط به یک موضوع خاص می
باشند و قرار است که منتشر بشوند.
چند نکته و توضیح
\- در نرم افزارها به جای چند جعبه که نوتها داخلش باشد از فولدر استفاده می کنیم
\- یک یادداشت می تواند در چندین موضع مرتبط باشد که می توانیم از هشتگ و لینک
استفاده کنیم
\- ممکن است یک یادداشت در چندین نقشه محتوایی نوشته شده باشد
\- من نقشه های محتوایی را هم بخش بندی کرده ام و شامل بخشهای خلاصه, مقدمه ,
تاریخچه , شرح , مقایسه , نتایج , نتیجه گیری - می شود که یادداشتهایی که اضافه
می کنم مربوط به همان بخش باشند و بعدا بتوانم به راحتی از آنها استفاده کنم
\- باید کاری کنید که همه یادداشتها متناوبا بارها مرور و خوانده شوند و اگر
یادداشتی بهش مراجعه نشود و مدت زیادی مرور نشود ممکن است بلا استفاده شود
\- خود نوت برداری شامل چندین مدل است
\- Outline Note-Taking Method
\- Cornell Note-Taking Method
\- Boxing Note-Taking Method
\- Charting Note-Taking Method
\- Mapping Note-Taking Method
\- Sentence Note-Taking Method
\- انواع طبقه بندی ها برای قسمت یادداشتهای دائمی
\- CODE
\- Capture: Saving valuable information from the internet and the world around
you
\- Organize: Breaking that information into small chunks and preparing them
for later use
\- Distill: Extracting the pieces of knowledge most relevant to your current
goals
\- Express: Turning your knowledge into creative output that has an impact on
others
\- PARA
\- Projects: series of tasks linked to a goal, with a deadline.
\- Areas: spheres of activity with a standard to be maintained over time.
\- Resources: topics or themes of ongoing interest.
\- Archives: inactive items from the other three categories.
\- productive
\- Home
\- Goals
\- Writings
\- Productivity
\- Creativity
\- Ideas
\- Zettelkasten
\- Fleeting Notes
\- Literature Notes
\- Permanent Notes / Evergreen
\- (MOC (map of content
\- Slip-box
مهمترین منبع آموزشی کتاب
title: How to take smart notes: One simple technique to boost writing,
learning and thinking
authors: Sönke Ahrens
year: 2022
[[@ahrens2022take]]
و برنامه رایگانی که ازش استفاده می کنم
OBSIDIAN
بخش اول رفرنسها می باشند. که من از برنامه رایگان
JabRef
استفاده می کنم و تمامی رفرنسهایی که ازشون استفاده کرده ام را داخلش می گذارم.
این برنامه به خوبی با برنامه های دیگر ترکیب می شود و می تواند به صورت بهینه و
اتوماتیک کار رفرنس دهی به متهای شما را انجام بدهد و فرمتهای مخلف را پشتیبانی
می کند
بعدا برای نوشتن مقاله یا کتاب نیازی به وارد کردن دستی رفرنسها و یا مرتب کردن و
عوض کردن فرمت ندارید
I use citation plugin
1\. add path to the JabRef database "reading notes/dh.bib"
2\. create folder "Reading notes"
3\. use Ctrl+Shift+O to select reference
4\. automatically create file based on that reference
5\. Ctrl+Shift+E to insert link to citation page
منابع
1\. تیزایران دات کام www.pirahansiah.com
2\. پیراهن سیاه دات کام www.pirahansiah.com
3\. https://www.notion.so/templates/second-brain
4\. https://filipedonadio.com/6-useful-templates-for-obsidian/
5\. https://www.youtube.com/watch?v=4aYVLpY5FYU
6\. http://luhmann.surge.sh/communicating-with-slip-boxes
7\. https://www.youtube.com/watch?v=4bxpsvcW2mc *
8\. https://www.youtube.com/channel/UC85D7ERwhke7wVqskV_DZUA
9\. https://www.youtube.com/watch?v=ftzQOkzGCLg
10\. https://writingcooperative.com/zettelkasten-how-one-german-scholar-was-
so-freakishly-productive-997e4e0ca125
11\. https://writingscientist.com/slip-box/
12\. [Zettlr is creating a Markdown editor for the 21st century |
Patreon](https://www.patreon.com/zettlr)
13\. [Choosing between Zettlr and Obsidian | Aquiles
Carattino](https://notes.aquiles.me/essays/choosing_between_zettlr_and_obsidian/#:~:text=Obsidian%20is%20more%20minimalistic%20than%20Zettlr.%20They%20both,see%20how%20notes%20are%20linked%20to%20each%20other.)
14\. [Zettlr vs Obsidian md detailed comparison as of 2022 -
Slant](https://www.slant.co/versus/31650/37045/~zettlr_vs_obsidian-md)
15\. https://hamed.blog/
16\. https://niklas-luhmann-archiv.de/nachlass/zettelkasten
17\. https://niklas-luhmann-archiv.de/bestand/zettelkasten/tutorial
18\. https://www.youtube.com/watch?v=D9ivU_IKO6M&ab_channel=ArtemKirsanov
19\. https://www.youtube.com/watch?v=TDhTpPIjsDg&ab_channel=JohnMavrick
20\. https://www.youtube.com/watch?v=-bdE_54UUA4&ab_channel=JohnMavrick
21\. https://forum.obsidian.md/t/example-workflows-in-obsidian/1093
22\. https://www.youtube.com/watch?v=0zrq2-06FTY&ab_channel=Aleks
23\. https://www.sloww.co/thinking-in-systems-book/
24\.
[https://www.sloww.co/zettelkasten/](https://www.google.com/url?q=https%3A%2F%2Fwww.sloww.co%2Fzettelkasten%2F&sa=D&sntz=1&usg=AOvVaw3NrwKNA-6R29660iQWnZcU)
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traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer Vision, Deep Learning,
Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
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* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep Learning, Artificial
superintelligence (ASI)](/home)
# How to make perfect resume (CV)
# Best template
All secret, tips and tricks for the interview
[https://vimeo.com/user118789239](https://www.google.com/url?q=https%3A%2F%2Fvimeo.com%2Fuser118789239&sa=D&sntz=1&usg=AOvVaw12_ck3O4dQG2ZMCP2VWOi3)
My experience in Germany
[https://europa.eu/europass/eportfolio/api/eprofile/shared-
profile/cbe41589-3c74-4743-9b08-8cab86ef932b?view=html](https://www.google.com/url?q=https%3A%2F%2Feuropa.eu%2Feuropass%2Feportfolio%2Fapi%2Feprofile%2Fshared-
profile%2Fcbe41589-3c74-4743-9b08-8cab86ef932b%3Fview%3Dhtml&sa=D&sntz=1&usg=AOvVaw2KTv6q596j2CEFJbGDdUuz)
#Metaverse #pirahansiah #CV #resume
* Very good YouTube channel about basic question in interview :
*
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traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# AltCoin
Disclaimer/ Risk warning:
I am not a financial advisor and anything you read, see or hear in this site,
podcast, video should not by any means be construed as financial advice it is
purely intended for your entertainment and demonstration and illustrative
purposes only.
This is not financial advice and should not be taken as financial advice. the
views I have in everyone of my
site/post/blog/links/text/documents/powerpoint/videos are completely
speculative opinions and do not guarantee any specific result. The NFT,
AltCoin, Metaverse, ... is extremely volatile and has high risk. You should
never act on anyone's advice or opinions, without first doing your own
research, realising your own risk, and making your own decision. I recommend
speaking with a licensed and qualified professional before making any
financial decision.
Basic
Links
Hands on
setup:macOS
1: install nvm: Node Version Manager
2\. install hardhat: Ethereum development environment for professionals
3.
METAVERSE
crypto 18.12.21 - 22.April.2022
NFT
Stock
* ^ Purchasing managers' indexes (PMI):
* A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month.
* ^ United States Philadelphia Fed Manufacturing Index
* A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.
*
My token _**pirahansiah (TIZ**_ _ **)**_ on Solana
Token name
pirahansiah (TIZ)
Token address
FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
**Token** **pirahansiah**
https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
My token _**pirahansiah (TIZ)**_
**Token Contract Address**
0xe30407DB873302D6AEaAB3bA619f44Bc9F924594
**Token Decimal:**
18
**Network:**
BNB Smart Chain Mainnet
only 100 token available to sell
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx-
uPjibkv51EdW)
[https://testnet.binance.org/faucet-
smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet-
smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
[https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK)
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
* how to modify the code
* [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market
* [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian
Interface: minimum data and functions required to make it a standard ERC/EIP
smart contract
_value the _ shows it is a parameters
Constant is a variable that can't be changed
Mapping() function maps elements from a key to a value
Constructor() function that automatically runs when a new data item is
created( initialization code)
Emit() function triggers an event (message to be sent out)
1. New project
1. ./geth --syncmode "light"
2. Mkdir
3. Truffle init
Truffle deploy --reset
Truffle console
HelloWorld.deployed().then(function(instance) {return instance} );
HelloWorld.deployed().then(function(instance) {return
instance.getHelloMessage()} );
npm install @truffle/hdwallet-provider
1. LinkedIn
1. Start 10.April.2022
2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK)
3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc)
4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A)
1. npm install truffle -g
5. ERC-20 (500K)
6. ERC721: Non-Fungible Tokens (NFT): 70K
7. ERC1155: Multi-Token Tokens : 8K
8. dApp
9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)
2. LinkedIn II
1. Hyperledger.org
2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n)
1. docker run ethereum/solc:stable --help
2. brew update
3. brew upgrade
4. brew tap ethereum/ethereum
5. brew install solidity
6.
4\. Visual studio code: Name: solidity
Id: JuanBlanco.solidity
Description: Ethereum Solidity Language for Visual Studio Code
Version: 0.0.139
Publisher: Juan Blanco
VS Marketplace Link:[
](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)
5\. Online editor:
[https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3)
اجماع
majority rules
validate transactions properly
computer nodes 51% consensus mechanisms
advantage proof of work:
* anybody can attached machines and gain rewards
blockchain trilemma: 1- scalable/speed 2- decentralization secure
fastest: solana (arweave),
[](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4
"Open Spreadsheet, AltCoin in new window")
AltCoin
#
Basic
[proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ)
#
Links
[https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa)
[https://chain.link/bootcamp/bootcamp-2021-on-
demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on-
demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf)
[https://software.intel.com/content/www/us/en/develop/download/download-
maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload-
maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn)
[آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم،
شماره
۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr)
[https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy)
#
Hands on
##
setup:macOS
###
1: install nvm: Node Version Manager
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh |
bash
nvm install 12
nvm use 12
nvm alias default 12
npm install npm --global # Upgrade npm to the latest version
###
2\. install hardhat: Ethereum development environment for professionals
npm install --save-dev hardhat
###
3\.
#
METAVERSE
1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto
* sandbox
* metaverse group buy decentraland: 2.43 M$
* [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR
Top 5 Land
1. * * 1. axie infinity
* savannah, forest, arctic, mystic, genesis, lunas landing
2. decentraland
*
* 9K land
* ~4500 MANA * 3$ = 13500$
* Decentraland Tutorials:
* my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2)
3. the sandbox
* ~2.5 Eth * 3850 = 9625
1. * * 4. bitcountry
* create and personalise metaverse
*
5. aavegotchi
* 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk)
* it is virtual world in ethereum 2011 - 2018 -
* open metaverse - the sandbox alpha - 29.11 to 20.12.21
* require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22)
*
3. learn:
4. learn:
5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh)
6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5)
##
crypto 18.12.21 - 22.April.2022
* Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022)
* sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022)
* Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022)
* illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022)
* star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022)
* wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022)
1. bitCoin : revolution to currency,
2. DeFi, ethereum
3. NFT
4. Metaverse
#
NFT
* [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ)
*
Links:
[https://github.com/MetaMask/metamask-
mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask-
mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4)
[https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_-
FuOwYHQEN)
[https://github.com/ish-
app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish-
app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t)
[https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX)
[https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS)
[https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE)
[https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP)
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[](/home)[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
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* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
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* [Apple](/topics-and-projects/apple)
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* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# AltCoin
Disclaimer/ Risk warning:
I am not a financial advisor and anything you read, see or hear in this site,
podcast, video should not by any means be construed as financial advice it is
purely intended for your entertainment and demonstration and illustrative
purposes only.
This is not financial advice and should not be taken as financial advice. the
views I have in everyone of my
site/post/blog/links/text/documents/powerpoint/videos are completely
speculative opinions and do not guarantee any specific result. The NFT,
AltCoin, Metaverse, ... is extremely volatile and has high risk. You should
never act on anyone's advice or opinions, without first doing your own
research, realising your own risk, and making your own decision. I recommend
speaking with a licensed and qualified professional before making any
financial decision.
Basic
Links
Hands on
setup:macOS
1: install nvm: Node Version Manager
2\. install hardhat: Ethereum development environment for professionals
3.
METAVERSE
crypto 18.12.21 - 22.April.2022
NFT
Stock
* ^ Purchasing managers' indexes (PMI):
* A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month.
* ^ United States Philadelphia Fed Manufacturing Index
* A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.
*
My token _**pirahansiah (TIZ**_ _ **)**_ on Solana
Token name
pirahansiah (TIZ)
Token address
FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
**Token** **pirahansiah**
https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
My token _**pirahansiah (TIZ)**_
**Token Contract Address**
0xe30407DB873302D6AEaAB3bA619f44Bc9F924594
**Token Decimal:**
18
**Network:**
BNB Smart Chain Mainnet
only 100 token available to sell
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx-
uPjibkv51EdW)
[https://testnet.binance.org/faucet-
smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet-
smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
[https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK)
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
* how to modify the code
* [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market
* [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian
Interface: minimum data and functions required to make it a standard ERC/EIP
smart contract
_value the _ shows it is a parameters
Constant is a variable that can't be changed
Mapping() function maps elements from a key to a value
Constructor() function that automatically runs when a new data item is
created( initialization code)
Emit() function triggers an event (message to be sent out)
1. New project
1. ./geth --syncmode "light"
2. Mkdir
3. Truffle init
Truffle deploy --reset
Truffle console
HelloWorld.deployed().then(function(instance) {return instance} );
HelloWorld.deployed().then(function(instance) {return
instance.getHelloMessage()} );
npm install @truffle/hdwallet-provider
1. LinkedIn
1. Start 10.April.2022
2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK)
3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc)
4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A)
1. npm install truffle -g
5. ERC-20 (500K)
6. ERC721: Non-Fungible Tokens (NFT): 70K
7. ERC1155: Multi-Token Tokens : 8K
8. dApp
9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)
2. LinkedIn II
1. Hyperledger.org
2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n)
1. docker run ethereum/solc:stable --help
2. brew update
3. brew upgrade
4. brew tap ethereum/ethereum
5. brew install solidity
6.
4\. Visual studio code: Name: solidity
Id: JuanBlanco.solidity
Description: Ethereum Solidity Language for Visual Studio Code
Version: 0.0.139
Publisher: Juan Blanco
VS Marketplace Link:[
](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)
5\. Online editor:
[https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3)
اجماع
majority rules
validate transactions properly
computer nodes 51% consensus mechanisms
advantage proof of work:
* anybody can attached machines and gain rewards
blockchain trilemma: 1- scalable/speed 2- decentralization secure
fastest: solana (arweave),
[](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4
"Open Spreadsheet, AltCoin in new window")
AltCoin
#
Basic
[proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ)
#
Links
[https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa)
[https://chain.link/bootcamp/bootcamp-2021-on-
demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on-
demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf)
[https://software.intel.com/content/www/us/en/develop/download/download-
maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload-
maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn)
[آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم،
شماره
۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr)
[https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy)
#
Hands on
##
setup:macOS
###
1: install nvm: Node Version Manager
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh |
bash
nvm install 12
nvm use 12
nvm alias default 12
npm install npm --global # Upgrade npm to the latest version
###
2\. install hardhat: Ethereum development environment for professionals
npm install --save-dev hardhat
###
3\.
#
METAVERSE
1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto
* sandbox
* metaverse group buy decentraland: 2.43 M$
* [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR
Top 5 Land
1. * * 1. axie infinity
* savannah, forest, arctic, mystic, genesis, lunas landing
2. decentraland
*
* 9K land
* ~4500 MANA * 3$ = 13500$
* Decentraland Tutorials:
* my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2)
3. the sandbox
* ~2.5 Eth * 3850 = 9625
1. * * 4. bitcountry
* create and personalise metaverse
*
5. aavegotchi
* 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk)
* it is virtual world in ethereum 2011 - 2018 -
* open metaverse - the sandbox alpha - 29.11 to 20.12.21
* require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22)
*
3. learn:
4. learn:
5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh)
6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5)
##
crypto 18.12.21 - 22.April.2022
* Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022)
* sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022)
* Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022)
* illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022)
* star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022)
* wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022)
1. bitCoin : revolution to currency,
2. DeFi, ethereum
3. NFT
4. Metaverse
#
NFT
* [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ)
*
Links:
[https://github.com/MetaMask/metamask-
mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask-
mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4)
[https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_-
FuOwYHQEN)
[https://github.com/ish-
app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish-
app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t)
[https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX)
[https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS)
[https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE)
[https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP)
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[](/home)[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
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* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# AltCoin
Disclaimer/ Risk warning:
I am not a financial advisor and anything you read, see or hear in this site,
podcast, video should not by any means be construed as financial advice it is
purely intended for your entertainment and demonstration and illustrative
purposes only.
This is not financial advice and should not be taken as financial advice. the
views I have in everyone of my
site/post/blog/links/text/documents/powerpoint/videos are completely
speculative opinions and do not guarantee any specific result. The NFT,
AltCoin, Metaverse, ... is extremely volatile and has high risk. You should
never act on anyone's advice or opinions, without first doing your own
research, realising your own risk, and making your own decision. I recommend
speaking with a licensed and qualified professional before making any
financial decision.
Basic
Links
Hands on
setup:macOS
1: install nvm: Node Version Manager
2\. install hardhat: Ethereum development environment for professionals
3.
METAVERSE
crypto 18.12.21 - 22.April.2022
NFT
Stock
* ^ Purchasing managers' indexes (PMI):
* A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month.
* ^ United States Philadelphia Fed Manufacturing Index
* A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.
*
My token _**pirahansiah (TIZ**_ _ **)**_ on Solana
Token name
pirahansiah (TIZ)
Token address
FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
**Token** **pirahansiah**
https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
My token _**pirahansiah (TIZ)**_
**Token Contract Address**
0xe30407DB873302D6AEaAB3bA619f44Bc9F924594
**Token Decimal:**
18
**Network:**
BNB Smart Chain Mainnet
only 100 token available to sell
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx-
uPjibkv51EdW)
[https://testnet.binance.org/faucet-
smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet-
smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
[https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK)
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
* how to modify the code
* [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market
* [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian
Interface: minimum data and functions required to make it a standard ERC/EIP
smart contract
_value the _ shows it is a parameters
Constant is a variable that can't be changed
Mapping() function maps elements from a key to a value
Constructor() function that automatically runs when a new data item is
created( initialization code)
Emit() function triggers an event (message to be sent out)
1. New project
1. ./geth --syncmode "light"
2. Mkdir
3. Truffle init
Truffle deploy --reset
Truffle console
HelloWorld.deployed().then(function(instance) {return instance} );
HelloWorld.deployed().then(function(instance) {return
instance.getHelloMessage()} );
npm install @truffle/hdwallet-provider
1. LinkedIn
1. Start 10.April.2022
2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK)
3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc)
4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A)
1. npm install truffle -g
5. ERC-20 (500K)
6. ERC721: Non-Fungible Tokens (NFT): 70K
7. ERC1155: Multi-Token Tokens : 8K
8. dApp
9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)
2. LinkedIn II
1. Hyperledger.org
2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n)
1. docker run ethereum/solc:stable --help
2. brew update
3. brew upgrade
4. brew tap ethereum/ethereum
5. brew install solidity
6.
4\. Visual studio code: Name: solidity
Id: JuanBlanco.solidity
Description: Ethereum Solidity Language for Visual Studio Code
Version: 0.0.139
Publisher: Juan Blanco
VS Marketplace Link:[
](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)
5\. Online editor:
[https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3)
اجماع
majority rules
validate transactions properly
computer nodes 51% consensus mechanisms
advantage proof of work:
* anybody can attached machines and gain rewards
blockchain trilemma: 1- scalable/speed 2- decentralization secure
fastest: solana (arweave),
[](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4
"Open Spreadsheet, AltCoin in new window")
AltCoin
#
Basic
[proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ)
#
Links
[https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa)
[https://chain.link/bootcamp/bootcamp-2021-on-
demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on-
demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf)
[https://software.intel.com/content/www/us/en/develop/download/download-
maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload-
maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn)
[آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم،
شماره
۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr)
[https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy)
#
Hands on
##
setup:macOS
###
1: install nvm: Node Version Manager
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh |
bash
nvm install 12
nvm use 12
nvm alias default 12
npm install npm --global # Upgrade npm to the latest version
###
2\. install hardhat: Ethereum development environment for professionals
npm install --save-dev hardhat
###
3\.
#
METAVERSE
1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto
* sandbox
* metaverse group buy decentraland: 2.43 M$
* [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR
Top 5 Land
1. * * 1. axie infinity
* savannah, forest, arctic, mystic, genesis, lunas landing
2. decentraland
*
* 9K land
* ~4500 MANA * 3$ = 13500$
* Decentraland Tutorials:
* my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2)
3. the sandbox
* ~2.5 Eth * 3850 = 9625
1. * * 4. bitcountry
* create and personalise metaverse
*
5. aavegotchi
* 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk)
* it is virtual world in ethereum 2011 - 2018 -
* open metaverse - the sandbox alpha - 29.11 to 20.12.21
* require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22)
*
3. learn:
4. learn:
5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh)
6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5)
##
crypto 18.12.21 - 22.April.2022
* Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022)
* sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022)
* Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022)
* illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022)
* star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022)
* wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022)
1. bitCoin : revolution to currency,
2. DeFi, ethereum
3. NFT
4. Metaverse
#
NFT
* [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ)
*
Links:
[https://github.com/MetaMask/metamask-
mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask-
mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4)
[https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_-
FuOwYHQEN)
[https://github.com/ish-
app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish-
app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t)
[https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX)
[https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS)
[https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE)
[https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP)
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[Computer Vision, Deep
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# AltCoin
Disclaimer/ Risk warning:
I am not a financial advisor and anything you read, see or hear in this site,
podcast, video should not by any means be construed as financial advice it is
purely intended for your entertainment and demonstration and illustrative
purposes only.
This is not financial advice and should not be taken as financial advice. the
views I have in everyone of my
site/post/blog/links/text/documents/powerpoint/videos are completely
speculative opinions and do not guarantee any specific result. The NFT,
AltCoin, Metaverse, ... is extremely volatile and has high risk. You should
never act on anyone's advice or opinions, without first doing your own
research, realising your own risk, and making your own decision. I recommend
speaking with a licensed and qualified professional before making any
financial decision.
Basic
Links
Hands on
setup:macOS
1: install nvm: Node Version Manager
2\. install hardhat: Ethereum development environment for professionals
3.
METAVERSE
crypto 18.12.21 - 22.April.2022
NFT
Stock
* ^ Purchasing managers' indexes (PMI):
* A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month.
* ^ United States Philadelphia Fed Manufacturing Index
* A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.
*
My token _**pirahansiah (TIZ**_ _ **)**_ on Solana
Token name
pirahansiah (TIZ)
Token address
FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
**Token** **pirahansiah**
https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
My token _**pirahansiah (TIZ)**_
**Token Contract Address**
0xe30407DB873302D6AEaAB3bA619f44Bc9F924594
**Token Decimal:**
18
**Network:**
BNB Smart Chain Mainnet
only 100 token available to sell
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx-
uPjibkv51EdW)
[https://testnet.binance.org/faucet-
smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet-
smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
[https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK)
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
* how to modify the code
* [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market
* [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian
Interface: minimum data and functions required to make it a standard ERC/EIP
smart contract
_value the _ shows it is a parameters
Constant is a variable that can't be changed
Mapping() function maps elements from a key to a value
Constructor() function that automatically runs when a new data item is
created( initialization code)
Emit() function triggers an event (message to be sent out)
1. New project
1. ./geth --syncmode "light"
2. Mkdir
3. Truffle init
Truffle deploy --reset
Truffle console
HelloWorld.deployed().then(function(instance) {return instance} );
HelloWorld.deployed().then(function(instance) {return
instance.getHelloMessage()} );
npm install @truffle/hdwallet-provider
1. LinkedIn
1. Start 10.April.2022
2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK)
3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc)
4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A)
1. npm install truffle -g
5. ERC-20 (500K)
6. ERC721: Non-Fungible Tokens (NFT): 70K
7. ERC1155: Multi-Token Tokens : 8K
8. dApp
9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)
2. LinkedIn II
1. Hyperledger.org
2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n)
1. docker run ethereum/solc:stable --help
2. brew update
3. brew upgrade
4. brew tap ethereum/ethereum
5. brew install solidity
6.
4\. Visual studio code: Name: solidity
Id: JuanBlanco.solidity
Description: Ethereum Solidity Language for Visual Studio Code
Version: 0.0.139
Publisher: Juan Blanco
VS Marketplace Link:[
](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)
5\. Online editor:
[https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3)
اجماع
majority rules
validate transactions properly
computer nodes 51% consensus mechanisms
advantage proof of work:
* anybody can attached machines and gain rewards
blockchain trilemma: 1- scalable/speed 2- decentralization secure
fastest: solana (arweave),
[](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4
"Open Spreadsheet, AltCoin in new window")
AltCoin
#
Basic
[proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ)
#
Links
[https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa)
[https://chain.link/bootcamp/bootcamp-2021-on-
demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on-
demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf)
[https://software.intel.com/content/www/us/en/develop/download/download-
maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload-
maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn)
[آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم،
شماره
۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr)
[https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy)
#
Hands on
##
setup:macOS
###
1: install nvm: Node Version Manager
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh |
bash
nvm install 12
nvm use 12
nvm alias default 12
npm install npm --global # Upgrade npm to the latest version
###
2\. install hardhat: Ethereum development environment for professionals
npm install --save-dev hardhat
###
3\.
#
METAVERSE
1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto
* sandbox
* metaverse group buy decentraland: 2.43 M$
* [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR
Top 5 Land
1. * * 1. axie infinity
* savannah, forest, arctic, mystic, genesis, lunas landing
2. decentraland
*
* 9K land
* ~4500 MANA * 3$ = 13500$
* Decentraland Tutorials:
* my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2)
3. the sandbox
* ~2.5 Eth * 3850 = 9625
1. * * 4. bitcountry
* create and personalise metaverse
*
5. aavegotchi
* 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk)
* it is virtual world in ethereum 2011 - 2018 -
* open metaverse - the sandbox alpha - 29.11 to 20.12.21
* require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22)
*
3. learn:
4. learn:
5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh)
6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5)
##
crypto 18.12.21 - 22.April.2022
* Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022)
* sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022)
* Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022)
* illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022)
* star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022)
* wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022)
1. bitCoin : revolution to currency,
2. DeFi, ethereum
3. NFT
4. Metaverse
#
NFT
* [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ)
*
Links:
[https://github.com/MetaMask/metamask-
mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask-
mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4)
[https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_-
FuOwYHQEN)
[https://github.com/ish-
app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish-
app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t)
[https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX)
[https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS)
[https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE)
[https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP)
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[Computer Vision, Deep
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# AltCoin
Disclaimer/ Risk warning:
I am not a financial advisor and anything you read, see or hear in this site,
podcast, video should not by any means be construed as financial advice it is
purely intended for your entertainment and demonstration and illustrative
purposes only.
This is not financial advice and should not be taken as financial advice. the
views I have in everyone of my
site/post/blog/links/text/documents/powerpoint/videos are completely
speculative opinions and do not guarantee any specific result. The NFT,
AltCoin, Metaverse, ... is extremely volatile and has high risk. You should
never act on anyone's advice or opinions, without first doing your own
research, realising your own risk, and making your own decision. I recommend
speaking with a licensed and qualified professional before making any
financial decision.
Basic
Links
Hands on
setup:macOS
1: install nvm: Node Version Manager
2\. install hardhat: Ethereum development environment for professionals
3.
METAVERSE
crypto 18.12.21 - 22.April.2022
NFT
Stock
* ^ Purchasing managers' indexes (PMI):
* A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month.
* ^ United States Philadelphia Fed Manufacturing Index
* A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.
*
My token _**pirahansiah (TIZ**_ _ **)**_ on Solana
Token name
pirahansiah (TIZ)
Token address
FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
**Token** **pirahansiah**
https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
My token _**pirahansiah (TIZ)**_
**Token Contract Address**
0xe30407DB873302D6AEaAB3bA619f44Bc9F924594
**Token Decimal:**
18
**Network:**
BNB Smart Chain Mainnet
only 100 token available to sell
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx-
uPjibkv51EdW)
[https://testnet.binance.org/faucet-
smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet-
smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
[https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK)
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
* how to modify the code
* [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market
* [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian
Interface: minimum data and functions required to make it a standard ERC/EIP
smart contract
_value the _ shows it is a parameters
Constant is a variable that can't be changed
Mapping() function maps elements from a key to a value
Constructor() function that automatically runs when a new data item is
created( initialization code)
Emit() function triggers an event (message to be sent out)
1. New project
1. ./geth --syncmode "light"
2. Mkdir
3. Truffle init
Truffle deploy --reset
Truffle console
HelloWorld.deployed().then(function(instance) {return instance} );
HelloWorld.deployed().then(function(instance) {return
instance.getHelloMessage()} );
npm install @truffle/hdwallet-provider
1. LinkedIn
1. Start 10.April.2022
2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK)
3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc)
4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A)
1. npm install truffle -g
5. ERC-20 (500K)
6. ERC721: Non-Fungible Tokens (NFT): 70K
7. ERC1155: Multi-Token Tokens : 8K
8. dApp
9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)
2. LinkedIn II
1. Hyperledger.org
2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n)
1. docker run ethereum/solc:stable --help
2. brew update
3. brew upgrade
4. brew tap ethereum/ethereum
5. brew install solidity
6.
4\. Visual studio code: Name: solidity
Id: JuanBlanco.solidity
Description: Ethereum Solidity Language for Visual Studio Code
Version: 0.0.139
Publisher: Juan Blanco
VS Marketplace Link:[
](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)
5\. Online editor:
[https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3)
اجماع
majority rules
validate transactions properly
computer nodes 51% consensus mechanisms
advantage proof of work:
* anybody can attached machines and gain rewards
blockchain trilemma: 1- scalable/speed 2- decentralization secure
fastest: solana (arweave),
[](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4
"Open Spreadsheet, AltCoin in new window")
AltCoin
#
Basic
[proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ)
#
Links
[https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa)
[https://chain.link/bootcamp/bootcamp-2021-on-
demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on-
demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf)
[https://software.intel.com/content/www/us/en/develop/download/download-
maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload-
maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn)
[آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم،
شماره
۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr)
[https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy)
#
Hands on
##
setup:macOS
###
1: install nvm: Node Version Manager
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh |
bash
nvm install 12
nvm use 12
nvm alias default 12
npm install npm --global # Upgrade npm to the latest version
###
2\. install hardhat: Ethereum development environment for professionals
npm install --save-dev hardhat
###
3\.
#
METAVERSE
1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto
* sandbox
* metaverse group buy decentraland: 2.43 M$
* [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR
Top 5 Land
1. * * 1. axie infinity
* savannah, forest, arctic, mystic, genesis, lunas landing
2. decentraland
*
* 9K land
* ~4500 MANA * 3$ = 13500$
* Decentraland Tutorials:
* my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2)
3. the sandbox
* ~2.5 Eth * 3850 = 9625
1. * * 4. bitcountry
* create and personalise metaverse
*
5. aavegotchi
* 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk)
* it is virtual world in ethereum 2011 - 2018 -
* open metaverse - the sandbox alpha - 29.11 to 20.12.21
* require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22)
*
3. learn:
4. learn:
5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh)
6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5)
##
crypto 18.12.21 - 22.April.2022
* Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022)
* sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022)
* Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022)
* illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022)
* star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022)
* wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022)
1. bitCoin : revolution to currency,
2. DeFi, ethereum
3. NFT
4. Metaverse
#
NFT
* [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ)
*
Links:
[https://github.com/MetaMask/metamask-
mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask-
mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4)
[https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_-
FuOwYHQEN)
[https://github.com/ish-
app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish-
app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t)
[https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX)
[https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS)
[https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE)
[https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP)
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[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# AltCoin
Disclaimer/ Risk warning:
I am not a financial advisor and anything you read, see or hear in this site,
podcast, video should not by any means be construed as financial advice it is
purely intended for your entertainment and demonstration and illustrative
purposes only.
This is not financial advice and should not be taken as financial advice. the
views I have in everyone of my
site/post/blog/links/text/documents/powerpoint/videos are completely
speculative opinions and do not guarantee any specific result. The NFT,
AltCoin, Metaverse, ... is extremely volatile and has high risk. You should
never act on anyone's advice or opinions, without first doing your own
research, realising your own risk, and making your own decision. I recommend
speaking with a licensed and qualified professional before making any
financial decision.
Basic
Links
Hands on
setup:macOS
1: install nvm: Node Version Manager
2\. install hardhat: Ethereum development environment for professionals
3.
METAVERSE
crypto 18.12.21 - 22.April.2022
NFT
Stock
* ^ Purchasing managers' indexes (PMI):
* A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month.
* ^ United States Philadelphia Fed Manufacturing Index
* A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.
*
My token _**pirahansiah (TIZ**_ _ **)**_ on Solana
Token name
pirahansiah (TIZ)
Token address
FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
**Token** **pirahansiah**
https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
My token _**pirahansiah (TIZ)**_
**Token Contract Address**
0xe30407DB873302D6AEaAB3bA619f44Bc9F924594
**Token Decimal:**
18
**Network:**
BNB Smart Chain Mainnet
only 100 token available to sell
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx-
uPjibkv51EdW)
[https://testnet.binance.org/faucet-
smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet-
smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
[https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK)
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
* how to modify the code
* [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market
* [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian
Interface: minimum data and functions required to make it a standard ERC/EIP
smart contract
_value the _ shows it is a parameters
Constant is a variable that can't be changed
Mapping() function maps elements from a key to a value
Constructor() function that automatically runs when a new data item is
created( initialization code)
Emit() function triggers an event (message to be sent out)
1. New project
1. ./geth --syncmode "light"
2. Mkdir
3. Truffle init
Truffle deploy --reset
Truffle console
HelloWorld.deployed().then(function(instance) {return instance} );
HelloWorld.deployed().then(function(instance) {return
instance.getHelloMessage()} );
npm install @truffle/hdwallet-provider
1. LinkedIn
1. Start 10.April.2022
2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK)
3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc)
4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A)
1. npm install truffle -g
5. ERC-20 (500K)
6. ERC721: Non-Fungible Tokens (NFT): 70K
7. ERC1155: Multi-Token Tokens : 8K
8. dApp
9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)
2. LinkedIn II
1. Hyperledger.org
2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n)
1. docker run ethereum/solc:stable --help
2. brew update
3. brew upgrade
4. brew tap ethereum/ethereum
5. brew install solidity
6.
4\. Visual studio code: Name: solidity
Id: JuanBlanco.solidity
Description: Ethereum Solidity Language for Visual Studio Code
Version: 0.0.139
Publisher: Juan Blanco
VS Marketplace Link:[
](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)
5\. Online editor:
[https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3)
اجماع
majority rules
validate transactions properly
computer nodes 51% consensus mechanisms
advantage proof of work:
* anybody can attached machines and gain rewards
blockchain trilemma: 1- scalable/speed 2- decentralization secure
fastest: solana (arweave),
[](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4
"Open Spreadsheet, AltCoin in new window")
AltCoin
#
Basic
[proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ)
#
Links
[https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa)
[https://chain.link/bootcamp/bootcamp-2021-on-
demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on-
demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf)
[https://software.intel.com/content/www/us/en/develop/download/download-
maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload-
maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn)
[آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم،
شماره
۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr)
[https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy)
#
Hands on
##
setup:macOS
###
1: install nvm: Node Version Manager
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh |
bash
nvm install 12
nvm use 12
nvm alias default 12
npm install npm --global # Upgrade npm to the latest version
###
2\. install hardhat: Ethereum development environment for professionals
npm install --save-dev hardhat
###
3\.
#
METAVERSE
1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto
* sandbox
* metaverse group buy decentraland: 2.43 M$
* [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR
Top 5 Land
1. * * 1. axie infinity
* savannah, forest, arctic, mystic, genesis, lunas landing
2. decentraland
*
* 9K land
* ~4500 MANA * 3$ = 13500$
* Decentraland Tutorials:
* my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2)
3. the sandbox
* ~2.5 Eth * 3850 = 9625
1. * * 4. bitcountry
* create and personalise metaverse
*
5. aavegotchi
* 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk)
* it is virtual world in ethereum 2011 - 2018 -
* open metaverse - the sandbox alpha - 29.11 to 20.12.21
* require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22)
*
3. learn:
4. learn:
5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh)
6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5)
##
crypto 18.12.21 - 22.April.2022
* Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022)
* sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022)
* Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022)
* illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022)
* star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022)
* wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022)
1. bitCoin : revolution to currency,
2. DeFi, ethereum
3. NFT
4. Metaverse
#
NFT
* [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ)
*
Links:
[https://github.com/MetaMask/metamask-
mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask-
mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4)
[https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_-
FuOwYHQEN)
[https://github.com/ish-
app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish-
app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t)
[https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX)
[https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS)
[https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE)
[https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP)
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[](/home)[Computer Vision, Deep
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* [Home](/home)
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* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# AltCoin
Disclaimer/ Risk warning:
I am not a financial advisor and anything you read, see or hear in this site,
podcast, video should not by any means be construed as financial advice it is
purely intended for your entertainment and demonstration and illustrative
purposes only.
This is not financial advice and should not be taken as financial advice. the
views I have in everyone of my
site/post/blog/links/text/documents/powerpoint/videos are completely
speculative opinions and do not guarantee any specific result. The NFT,
AltCoin, Metaverse, ... is extremely volatile and has high risk. You should
never act on anyone's advice or opinions, without first doing your own
research, realising your own risk, and making your own decision. I recommend
speaking with a licensed and qualified professional before making any
financial decision.
Basic
Links
Hands on
setup:macOS
1: install nvm: Node Version Manager
2\. install hardhat: Ethereum development environment for professionals
3.
METAVERSE
crypto 18.12.21 - 22.April.2022
NFT
Stock
* ^ Purchasing managers' indexes (PMI):
* A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month.
* ^ United States Philadelphia Fed Manufacturing Index
* A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.
*
My token _**pirahansiah (TIZ**_ _ **)**_ on Solana
Token name
pirahansiah (TIZ)
Token address
FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
**Token** **pirahansiah**
https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
My token _**pirahansiah (TIZ)**_
**Token Contract Address**
0xe30407DB873302D6AEaAB3bA619f44Bc9F924594
**Token Decimal:**
18
**Network:**
BNB Smart Chain Mainnet
only 100 token available to sell
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx-
uPjibkv51EdW)
[https://testnet.binance.org/faucet-
smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet-
smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
[https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK)
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
* how to modify the code
* [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market
* [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian
Interface: minimum data and functions required to make it a standard ERC/EIP
smart contract
_value the _ shows it is a parameters
Constant is a variable that can't be changed
Mapping() function maps elements from a key to a value
Constructor() function that automatically runs when a new data item is
created( initialization code)
Emit() function triggers an event (message to be sent out)
1. New project
1. ./geth --syncmode "light"
2. Mkdir
3. Truffle init
Truffle deploy --reset
Truffle console
HelloWorld.deployed().then(function(instance) {return instance} );
HelloWorld.deployed().then(function(instance) {return
instance.getHelloMessage()} );
npm install @truffle/hdwallet-provider
1. LinkedIn
1. Start 10.April.2022
2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK)
3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc)
4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A)
1. npm install truffle -g
5. ERC-20 (500K)
6. ERC721: Non-Fungible Tokens (NFT): 70K
7. ERC1155: Multi-Token Tokens : 8K
8. dApp
9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)
2. LinkedIn II
1. Hyperledger.org
2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n)
1. docker run ethereum/solc:stable --help
2. brew update
3. brew upgrade
4. brew tap ethereum/ethereum
5. brew install solidity
6.
4\. Visual studio code: Name: solidity
Id: JuanBlanco.solidity
Description: Ethereum Solidity Language for Visual Studio Code
Version: 0.0.139
Publisher: Juan Blanco
VS Marketplace Link:[
](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)
5\. Online editor:
[https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3)
اجماع
majority rules
validate transactions properly
computer nodes 51% consensus mechanisms
advantage proof of work:
* anybody can attached machines and gain rewards
blockchain trilemma: 1- scalable/speed 2- decentralization secure
fastest: solana (arweave),
[](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4
"Open Spreadsheet, AltCoin in new window")
AltCoin
#
Basic
[proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ)
#
Links
[https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa)
[https://chain.link/bootcamp/bootcamp-2021-on-
demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on-
demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf)
[https://software.intel.com/content/www/us/en/develop/download/download-
maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload-
maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn)
[آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم،
شماره
۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr)
[https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy)
#
Hands on
##
setup:macOS
###
1: install nvm: Node Version Manager
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh |
bash
nvm install 12
nvm use 12
nvm alias default 12
npm install npm --global # Upgrade npm to the latest version
###
2\. install hardhat: Ethereum development environment for professionals
npm install --save-dev hardhat
###
3\.
#
METAVERSE
1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto
* sandbox
* metaverse group buy decentraland: 2.43 M$
* [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR
Top 5 Land
1. * * 1. axie infinity
* savannah, forest, arctic, mystic, genesis, lunas landing
2. decentraland
*
* 9K land
* ~4500 MANA * 3$ = 13500$
* Decentraland Tutorials:
* my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2)
3. the sandbox
* ~2.5 Eth * 3850 = 9625
1. * * 4. bitcountry
* create and personalise metaverse
*
5. aavegotchi
* 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk)
* it is virtual world in ethereum 2011 - 2018 -
* open metaverse - the sandbox alpha - 29.11 to 20.12.21
* require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22)
*
3. learn:
4. learn:
5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh)
6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5)
##
crypto 18.12.21 - 22.April.2022
* Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022)
* sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022)
* Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022)
* illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022)
* star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022)
* wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022)
1. bitCoin : revolution to currency,
2. DeFi, ethereum
3. NFT
4. Metaverse
#
NFT
* [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ)
*
Links:
[https://github.com/MetaMask/metamask-
mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask-
mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4)
[https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_-
FuOwYHQEN)
[https://github.com/ish-
app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish-
app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t)
[https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX)
[https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS)
[https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE)
[https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP)
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[](/home)[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
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* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# AltCoin
Disclaimer/ Risk warning:
I am not a financial advisor and anything you read, see or hear in this site,
podcast, video should not by any means be construed as financial advice it is
purely intended for your entertainment and demonstration and illustrative
purposes only.
This is not financial advice and should not be taken as financial advice. the
views I have in everyone of my
site/post/blog/links/text/documents/powerpoint/videos are completely
speculative opinions and do not guarantee any specific result. The NFT,
AltCoin, Metaverse, ... is extremely volatile and has high risk. You should
never act on anyone's advice or opinions, without first doing your own
research, realising your own risk, and making your own decision. I recommend
speaking with a licensed and qualified professional before making any
financial decision.
Basic
Links
Hands on
setup:macOS
1: install nvm: Node Version Manager
2\. install hardhat: Ethereum development environment for professionals
3.
METAVERSE
crypto 18.12.21 - 22.April.2022
NFT
Stock
* ^ Purchasing managers' indexes (PMI):
* A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month.
* ^ United States Philadelphia Fed Manufacturing Index
* A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.
*
My token _**pirahansiah (TIZ**_ _ **)**_ on Solana
Token name
pirahansiah (TIZ)
Token address
FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
**Token** **pirahansiah**
https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
My token _**pirahansiah (TIZ)**_
**Token Contract Address**
0xe30407DB873302D6AEaAB3bA619f44Bc9F924594
**Token Decimal:**
18
**Network:**
BNB Smart Chain Mainnet
only 100 token available to sell
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx-
uPjibkv51EdW)
[https://testnet.binance.org/faucet-
smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet-
smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
[https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK)
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
* how to modify the code
* [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market
* [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian
Interface: minimum data and functions required to make it a standard ERC/EIP
smart contract
_value the _ shows it is a parameters
Constant is a variable that can't be changed
Mapping() function maps elements from a key to a value
Constructor() function that automatically runs when a new data item is
created( initialization code)
Emit() function triggers an event (message to be sent out)
1. New project
1. ./geth --syncmode "light"
2. Mkdir
3. Truffle init
Truffle deploy --reset
Truffle console
HelloWorld.deployed().then(function(instance) {return instance} );
HelloWorld.deployed().then(function(instance) {return
instance.getHelloMessage()} );
npm install @truffle/hdwallet-provider
1. LinkedIn
1. Start 10.April.2022
2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK)
3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc)
4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A)
1. npm install truffle -g
5. ERC-20 (500K)
6. ERC721: Non-Fungible Tokens (NFT): 70K
7. ERC1155: Multi-Token Tokens : 8K
8. dApp
9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)
2. LinkedIn II
1. Hyperledger.org
2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n)
1. docker run ethereum/solc:stable --help
2. brew update
3. brew upgrade
4. brew tap ethereum/ethereum
5. brew install solidity
6.
4\. Visual studio code: Name: solidity
Id: JuanBlanco.solidity
Description: Ethereum Solidity Language for Visual Studio Code
Version: 0.0.139
Publisher: Juan Blanco
VS Marketplace Link:[
](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)
5\. Online editor:
[https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3)
اجماع
majority rules
validate transactions properly
computer nodes 51% consensus mechanisms
advantage proof of work:
* anybody can attached machines and gain rewards
blockchain trilemma: 1- scalable/speed 2- decentralization secure
fastest: solana (arweave),
[](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4
"Open Spreadsheet, AltCoin in new window")
AltCoin
#
Basic
[proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ)
#
Links
[https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa)
[https://chain.link/bootcamp/bootcamp-2021-on-
demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on-
demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf)
[https://software.intel.com/content/www/us/en/develop/download/download-
maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload-
maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn)
[آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم،
شماره
۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr)
[https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy)
#
Hands on
##
setup:macOS
###
1: install nvm: Node Version Manager
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh |
bash
nvm install 12
nvm use 12
nvm alias default 12
npm install npm --global # Upgrade npm to the latest version
###
2\. install hardhat: Ethereum development environment for professionals
npm install --save-dev hardhat
###
3\.
#
METAVERSE
1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto
* sandbox
* metaverse group buy decentraland: 2.43 M$
* [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR
Top 5 Land
1. * * 1. axie infinity
* savannah, forest, arctic, mystic, genesis, lunas landing
2. decentraland
*
* 9K land
* ~4500 MANA * 3$ = 13500$
* Decentraland Tutorials:
* my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2)
3. the sandbox
* ~2.5 Eth * 3850 = 9625
1. * * 4. bitcountry
* create and personalise metaverse
*
5. aavegotchi
* 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk)
* it is virtual world in ethereum 2011 - 2018 -
* open metaverse - the sandbox alpha - 29.11 to 20.12.21
* require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22)
*
3. learn:
4. learn:
5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh)
6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5)
##
crypto 18.12.21 - 22.April.2022
* Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022)
* sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022)
* Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022)
* illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022)
* star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022)
* wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022)
1. bitCoin : revolution to currency,
2. DeFi, ethereum
3. NFT
4. Metaverse
#
NFT
* [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ)
*
Links:
[https://github.com/MetaMask/metamask-
mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask-
mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4)
[https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_-
FuOwYHQEN)
[https://github.com/ish-
app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish-
app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t)
[https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX)
[https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS)
[https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE)
[https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP)
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[](/home)[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
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* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# AltCoin
Disclaimer/ Risk warning:
I am not a financial advisor and anything you read, see or hear in this site,
podcast, video should not by any means be construed as financial advice it is
purely intended for your entertainment and demonstration and illustrative
purposes only.
This is not financial advice and should not be taken as financial advice. the
views I have in everyone of my
site/post/blog/links/text/documents/powerpoint/videos are completely
speculative opinions and do not guarantee any specific result. The NFT,
AltCoin, Metaverse, ... is extremely volatile and has high risk. You should
never act on anyone's advice or opinions, without first doing your own
research, realising your own risk, and making your own decision. I recommend
speaking with a licensed and qualified professional before making any
financial decision.
Basic
Links
Hands on
setup:macOS
1: install nvm: Node Version Manager
2\. install hardhat: Ethereum development environment for professionals
3.
METAVERSE
crypto 18.12.21 - 22.April.2022
NFT
Stock
* ^ Purchasing managers' indexes (PMI):
* A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month.
* ^ United States Philadelphia Fed Manufacturing Index
* A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.
*
My token _**pirahansiah (TIZ**_ _ **)**_ on Solana
Token name
pirahansiah (TIZ)
Token address
FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
**Token** **pirahansiah**
https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
My token _**pirahansiah (TIZ)**_
**Token Contract Address**
0xe30407DB873302D6AEaAB3bA619f44Bc9F924594
**Token Decimal:**
18
**Network:**
BNB Smart Chain Mainnet
only 100 token available to sell
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx-
uPjibkv51EdW)
[https://testnet.binance.org/faucet-
smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet-
smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
[https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK)
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
* how to modify the code
* [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market
* [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian
Interface: minimum data and functions required to make it a standard ERC/EIP
smart contract
_value the _ shows it is a parameters
Constant is a variable that can't be changed
Mapping() function maps elements from a key to a value
Constructor() function that automatically runs when a new data item is
created( initialization code)
Emit() function triggers an event (message to be sent out)
1. New project
1. ./geth --syncmode "light"
2. Mkdir
3. Truffle init
Truffle deploy --reset
Truffle console
HelloWorld.deployed().then(function(instance) {return instance} );
HelloWorld.deployed().then(function(instance) {return
instance.getHelloMessage()} );
npm install @truffle/hdwallet-provider
1. LinkedIn
1. Start 10.April.2022
2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK)
3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc)
4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A)
1. npm install truffle -g
5. ERC-20 (500K)
6. ERC721: Non-Fungible Tokens (NFT): 70K
7. ERC1155: Multi-Token Tokens : 8K
8. dApp
9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)
2. LinkedIn II
1. Hyperledger.org
2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n)
1. docker run ethereum/solc:stable --help
2. brew update
3. brew upgrade
4. brew tap ethereum/ethereum
5. brew install solidity
6.
4\. Visual studio code: Name: solidity
Id: JuanBlanco.solidity
Description: Ethereum Solidity Language for Visual Studio Code
Version: 0.0.139
Publisher: Juan Blanco
VS Marketplace Link:[
](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)
5\. Online editor:
[https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3)
اجماع
majority rules
validate transactions properly
computer nodes 51% consensus mechanisms
advantage proof of work:
* anybody can attached machines and gain rewards
blockchain trilemma: 1- scalable/speed 2- decentralization secure
fastest: solana (arweave),
[](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4
"Open Spreadsheet, AltCoin in new window")
AltCoin
#
Basic
[proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ)
#
Links
[https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa)
[https://chain.link/bootcamp/bootcamp-2021-on-
demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on-
demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf)
[https://software.intel.com/content/www/us/en/develop/download/download-
maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload-
maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn)
[آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم،
شماره
۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr)
[https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy)
#
Hands on
##
setup:macOS
###
1: install nvm: Node Version Manager
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh |
bash
nvm install 12
nvm use 12
nvm alias default 12
npm install npm --global # Upgrade npm to the latest version
###
2\. install hardhat: Ethereum development environment for professionals
npm install --save-dev hardhat
###
3\.
#
METAVERSE
1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto
* sandbox
* metaverse group buy decentraland: 2.43 M$
* [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR
Top 5 Land
1. * * 1. axie infinity
* savannah, forest, arctic, mystic, genesis, lunas landing
2. decentraland
*
* 9K land
* ~4500 MANA * 3$ = 13500$
* Decentraland Tutorials:
* my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2)
3. the sandbox
* ~2.5 Eth * 3850 = 9625
1. * * 4. bitcountry
* create and personalise metaverse
*
5. aavegotchi
* 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk)
* it is virtual world in ethereum 2011 - 2018 -
* open metaverse - the sandbox alpha - 29.11 to 20.12.21
* require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22)
*
3. learn:
4. learn:
5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh)
6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5)
##
crypto 18.12.21 - 22.April.2022
* Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022)
* sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022)
* Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022)
* illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022)
* star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022)
* wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022)
1. bitCoin : revolution to currency,
2. DeFi, ethereum
3. NFT
4. Metaverse
#
NFT
* [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ)
*
Links:
[https://github.com/MetaMask/metamask-
mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask-
mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4)
[https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_-
FuOwYHQEN)
[https://github.com/ish-
app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish-
app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t)
[https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX)
[https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS)
[https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE)
[https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP)
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[](/home)[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
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* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# AltCoin
Disclaimer/ Risk warning:
I am not a financial advisor and anything you read, see or hear in this site,
podcast, video should not by any means be construed as financial advice it is
purely intended for your entertainment and demonstration and illustrative
purposes only.
This is not financial advice and should not be taken as financial advice. the
views I have in everyone of my
site/post/blog/links/text/documents/powerpoint/videos are completely
speculative opinions and do not guarantee any specific result. The NFT,
AltCoin, Metaverse, ... is extremely volatile and has high risk. You should
never act on anyone's advice or opinions, without first doing your own
research, realising your own risk, and making your own decision. I recommend
speaking with a licensed and qualified professional before making any
financial decision.
Basic
Links
Hands on
setup:macOS
1: install nvm: Node Version Manager
2\. install hardhat: Ethereum development environment for professionals
3.
METAVERSE
crypto 18.12.21 - 22.April.2022
NFT
Stock
* ^ Purchasing managers' indexes (PMI):
* A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month.
* ^ United States Philadelphia Fed Manufacturing Index
* A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.
*
My token _**pirahansiah (TIZ**_ _ **)**_ on Solana
Token name
pirahansiah (TIZ)
Token address
FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
**Token** **pirahansiah**
https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
My token _**pirahansiah (TIZ)**_
**Token Contract Address**
0xe30407DB873302D6AEaAB3bA619f44Bc9F924594
**Token Decimal:**
18
**Network:**
BNB Smart Chain Mainnet
only 100 token available to sell
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx-
uPjibkv51EdW)
[https://testnet.binance.org/faucet-
smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet-
smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
[https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK)
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
* how to modify the code
* [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market
* [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian
Interface: minimum data and functions required to make it a standard ERC/EIP
smart contract
_value the _ shows it is a parameters
Constant is a variable that can't be changed
Mapping() function maps elements from a key to a value
Constructor() function that automatically runs when a new data item is
created( initialization code)
Emit() function triggers an event (message to be sent out)
1. New project
1. ./geth --syncmode "light"
2. Mkdir
3. Truffle init
Truffle deploy --reset
Truffle console
HelloWorld.deployed().then(function(instance) {return instance} );
HelloWorld.deployed().then(function(instance) {return
instance.getHelloMessage()} );
npm install @truffle/hdwallet-provider
1. LinkedIn
1. Start 10.April.2022
2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK)
3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc)
4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A)
1. npm install truffle -g
5. ERC-20 (500K)
6. ERC721: Non-Fungible Tokens (NFT): 70K
7. ERC1155: Multi-Token Tokens : 8K
8. dApp
9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)
2. LinkedIn II
1. Hyperledger.org
2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n)
1. docker run ethereum/solc:stable --help
2. brew update
3. brew upgrade
4. brew tap ethereum/ethereum
5. brew install solidity
6.
4\. Visual studio code: Name: solidity
Id: JuanBlanco.solidity
Description: Ethereum Solidity Language for Visual Studio Code
Version: 0.0.139
Publisher: Juan Blanco
VS Marketplace Link:[
](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)
5\. Online editor:
[https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3)
اجماع
majority rules
validate transactions properly
computer nodes 51% consensus mechanisms
advantage proof of work:
* anybody can attached machines and gain rewards
blockchain trilemma: 1- scalable/speed 2- decentralization secure
fastest: solana (arweave),
[](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4
"Open Spreadsheet, AltCoin in new window")
AltCoin
#
Basic
[proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ)
#
Links
[https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa)
[https://chain.link/bootcamp/bootcamp-2021-on-
demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on-
demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf)
[https://software.intel.com/content/www/us/en/develop/download/download-
maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload-
maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn)
[آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم،
شماره
۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr)
[https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy)
#
Hands on
##
setup:macOS
###
1: install nvm: Node Version Manager
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh |
bash
nvm install 12
nvm use 12
nvm alias default 12
npm install npm --global # Upgrade npm to the latest version
###
2\. install hardhat: Ethereum development environment for professionals
npm install --save-dev hardhat
###
3\.
#
METAVERSE
1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto
* sandbox
* metaverse group buy decentraland: 2.43 M$
* [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR
Top 5 Land
1. * * 1. axie infinity
* savannah, forest, arctic, mystic, genesis, lunas landing
2. decentraland
*
* 9K land
* ~4500 MANA * 3$ = 13500$
* Decentraland Tutorials:
* my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2)
3. the sandbox
* ~2.5 Eth * 3850 = 9625
1. * * 4. bitcountry
* create and personalise metaverse
*
5. aavegotchi
* 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk)
* it is virtual world in ethereum 2011 - 2018 -
* open metaverse - the sandbox alpha - 29.11 to 20.12.21
* require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22)
*
3. learn:
4. learn:
5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh)
6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5)
##
crypto 18.12.21 - 22.April.2022
* Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022)
* sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022)
* Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022)
* illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022)
* star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022)
* wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022)
1. bitCoin : revolution to currency,
2. DeFi, ethereum
3. NFT
4. Metaverse
#
NFT
* [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ)
*
Links:
[https://github.com/MetaMask/metamask-
mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask-
mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4)
[https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_-
FuOwYHQEN)
[https://github.com/ish-
app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish-
app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t)
[https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX)
[https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS)
[https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE)
[https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP)
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[Computer Vision, Deep Learning, Artificial
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# AltCoin
Disclaimer/ Risk warning:
I am not a financial advisor and anything you read, see or hear in this site,
podcast, video should not by any means be construed as financial advice it is
purely intended for your entertainment and demonstration and illustrative
purposes only.
This is not financial advice and should not be taken as financial advice. the
views I have in everyone of my
site/post/blog/links/text/documents/powerpoint/videos are completely
speculative opinions and do not guarantee any specific result. The NFT,
AltCoin, Metaverse, ... is extremely volatile and has high risk. You should
never act on anyone's advice or opinions, without first doing your own
research, realising your own risk, and making your own decision. I recommend
speaking with a licensed and qualified professional before making any
financial decision.
Basic
Links
Hands on
setup:macOS
1: install nvm: Node Version Manager
2\. install hardhat: Ethereum development environment for professionals
3.
METAVERSE
crypto 18.12.21 - 22.April.2022
NFT
Stock
* ^ Purchasing managers' indexes (PMI):
* A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month.
* ^ United States Philadelphia Fed Manufacturing Index
* A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.
*
My token _**pirahansiah (TIZ**_ _ **)**_ on Solana
Token name
pirahansiah (TIZ)
Token address
FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
**Token** **pirahansiah**
https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in
My token _**pirahansiah (TIZ)**_
**Token Contract Address**
0xe30407DB873302D6AEaAB3bA619f44Bc9F924594
**Token Decimal:**
18
**Network:**
BNB Smart Chain Mainnet
only 100 token available to sell
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx-
uPjibkv51EdW)
[https://testnet.binance.org/faucet-
smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet-
smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
[https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK)
[https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS-
gyWJtD_682hE)
[https://github.com/OpenZeppelin/openzeppelin-
contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin-
contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv-
DNK3whJT2)
* how to modify the code
* [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market
* [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian
Interface: minimum data and functions required to make it a standard ERC/EIP
smart contract
_value the _ shows it is a parameters
Constant is a variable that can't be changed
Mapping() function maps elements from a key to a value
Constructor() function that automatically runs when a new data item is
created( initialization code)
Emit() function triggers an event (message to be sent out)
1. New project
1. ./geth --syncmode "light"
2. Mkdir
3. Truffle init
Truffle deploy --reset
Truffle console
HelloWorld.deployed().then(function(instance) {return instance} );
HelloWorld.deployed().then(function(instance) {return
instance.getHelloMessage()} );
npm install @truffle/hdwallet-provider
1. LinkedIn
1. Start 10.April.2022
2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK)
3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc)
4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A)
1. npm install truffle -g
5. ERC-20 (500K)
6. ERC721: Non-Fungible Tokens (NFT): 70K
7. ERC1155: Multi-Token Tokens : 8K
8. dApp
9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)
2. LinkedIn II
1. Hyperledger.org
2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n)
1. docker run ethereum/solc:stable --help
2. brew update
3. brew upgrade
4. brew tap ethereum/ethereum
5. brew install solidity
6.
4\. Visual studio code: Name: solidity
Id: JuanBlanco.solidity
Description: Ethereum Solidity Language for Visual Studio Code
Version: 0.0.139
Publisher: Juan Blanco
VS Marketplace Link:[
](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)
5\. Online editor:
[https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3)
اجماع
majority rules
validate transactions properly
computer nodes 51% consensus mechanisms
advantage proof of work:
* anybody can attached machines and gain rewards
blockchain trilemma: 1- scalable/speed 2- decentralization secure
fastest: solana (arweave),
[](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4
"Open Spreadsheet, AltCoin in new window")
AltCoin
#
Basic
[proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ)
#
Links
[https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa)
[https://chain.link/bootcamp/bootcamp-2021-on-
demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on-
demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf)
[https://software.intel.com/content/www/us/en/develop/download/download-
maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload-
maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn)
[آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم،
شماره
۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr)
[https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy)
#
Hands on
##
setup:macOS
###
1: install nvm: Node Version Manager
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh |
bash
nvm install 12
nvm use 12
nvm alias default 12
npm install npm --global # Upgrade npm to the latest version
###
2\. install hardhat: Ethereum development environment for professionals
npm install --save-dev hardhat
###
3\.
#
METAVERSE
1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto
* sandbox
* metaverse group buy decentraland: 2.43 M$
* [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR
Top 5 Land
1. * * 1. axie infinity
* savannah, forest, arctic, mystic, genesis, lunas landing
2. decentraland
*
* 9K land
* ~4500 MANA * 3$ = 13500$
* Decentraland Tutorials:
* my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2)
3. the sandbox
* ~2.5 Eth * 3850 = 9625
1. * * 4. bitcountry
* create and personalise metaverse
*
5. aavegotchi
* 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk)
* it is virtual world in ethereum 2011 - 2018 -
* open metaverse - the sandbox alpha - 29.11 to 20.12.21
* require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22)
*
3. learn:
4. learn:
5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh)
6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5)
##
crypto 18.12.21 - 22.April.2022
* Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022)
* sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022)
* Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022)
* illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022)
* star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022)
* wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022)
1. bitCoin : revolution to currency,
2. DeFi, ethereum
3. NFT
4. Metaverse
#
NFT
* [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ)
*
Links:
[https://github.com/MetaMask/metamask-
mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask-
mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4)
[https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_-
FuOwYHQEN)
[https://github.com/ish-
app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish-
app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t)
[https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX)
[https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS)
[https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE)
[https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP)
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# Quantum Computing
* A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
* [https://github.com/quantumlib/Cirq](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fquantumlib%2FCirq&sa=D&sntz=1&usg=AOvVaw0biGo5jjMCoK43H0skLzyQ)
* two startups,[ ](https://www.google.com/url?q=https%3A%2F%2Fionq.com%2F&sa=D&sntz=1&usg=AOvVaw1FvQZ7oh_Joec2jDy7Jo-r)[IonQ](https://www.google.com/url?q=https%3A%2F%2Fionq.com%2F&sa=D&sntz=1&usg=AOvVaw1FvQZ7oh_Joec2jDy7Jo-r), from the University of Maryland, and[ ](https://www.google.com/url?q=https%3A%2F%2Fquantumcircuits.com%2F&sa=D&sntz=1&usg=AOvVaw2q3lwVp5BnYhvbHBLKG7nl)[QCI](https://www.google.com/url?q=https%3A%2F%2Fquantumcircuits.com%2F&sa=D&sntz=1&usg=AOvVaw2q3lwVp5BnYhvbHBLKG7nl) from Yale, to achieve quantum computing capabilities.
* [https://techgrabyte.com/microsoft-azure-quantum-cloud-ecosystem/?fbclid=IwAR3BP6aczX_mugR_gB3HFh2Lzia6FusqpG1awrMOQUhx7R8Nhinw6GTsxt4](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fmicrosoft-azure-quantum-cloud-ecosystem%2F%3Ffbclid%3DIwAR3BP6aczX_mugR_gB3HFh2Lzia6FusqpG1awrMOQUhx7R8Nhinw6GTsxt4&sa=D&sntz=1&usg=AOvVaw22GJMbRyzFPr7ZbaXA1spw)
* The Quantum Development Kit (QDK) is interoperable with Python, and it aims to abstract differences that exist between different types of quantum computers. Both the Q# and the Quantum Development Kit can be tested on simulators as well as on a variety of quantum hardware.
* Microsoft has a three-pronged goal with Azure Quantum. It can be used for learning, developers can write programs with Q# and the QDK and test their codes against simulators and organizations can use them to solve complex business problems using solutions and algorithms running in Azure.
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
[#computervision](https://www.linkedin.com/feed/hashtag/?keywords=computervision&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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[#cameracalibration](https://www.linkedin.com/feed/hashtag/?keywords=cameracalibration&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
[#opticalflow](https://www.linkedin.com/feed/hashtag/?keywords=opticalflow&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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[#localization](https://www.linkedin.com/feed/hashtag/?keywords=localization&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
[#3dSLAM](https://www.linkedin.com/feed/hashtag/?keywords=3dslam&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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[#c](https://www.linkedin.com/feed/hashtag/?keywords=c&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)++
[#python](https://www.linkedin.com/feed/hashtag/?keywords=python&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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[#deeplearning](https://www.linkedin.com/feed/hashtag/?keywords=deeplearning&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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[#depthmap](https://www.linkedin.com/feed/hashtag/?keywords=depthmap&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
* [fulfillmentcrowd](https://www.fulfilmentcrowd.com/)
* [ChinaDivision](https://www.chinadivision.com/)
* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
[#computervision](https://www.linkedin.com/feed/hashtag/?keywords=computervision&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
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* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
[#computervision](https://www.linkedin.com/feed/hashtag/?keywords=computervision&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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[#c](https://www.linkedin.com/feed/hashtag/?keywords=c&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)++
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
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* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
* [fulfillmentcrowd](https://www.fulfilmentcrowd.com/)
* [ChinaDivision](https://www.chinadivision.com/)
* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
[#computervision](https://www.linkedin.com/feed/hashtag/?keywords=computervision&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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#pirahansiah.com #farshid #pirahansiah
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[#deeplearning](https://www.linkedin.com/feed/hashtag/?keywords=deeplearning&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
* [fulfillmentcrowd](https://www.fulfilmentcrowd.com/)
* [ChinaDivision](https://www.chinadivision.com/)
* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
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#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
* [fulfillmentcrowd](https://www.fulfilmentcrowd.com/)
* [ChinaDivision](https://www.chinadivision.com/)
* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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[Computer
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
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[#imageprocessing](https://www.linkedin.com/feed/hashtag/?keywords=imageprocessing&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
[#patternrecognition](https://www.linkedin.com/feed/hashtag/?keywords=patternrecognition&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
[#compiler](https://www.linkedin.com/feed/hashtag/?keywords=compiler&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
[#RISC](https://www.linkedin.com/feed/hashtag/?keywords=risc&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)-V
[#RNN](https://www.linkedin.com/feed/hashtag/?keywords=rnn&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
* [fulfillmentcrowd](https://www.fulfilmentcrowd.com/)
* [ChinaDivision](https://www.chinadivision.com/)
* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
[#computervision](https://www.linkedin.com/feed/hashtag/?keywords=computervision&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
* [fulfillmentcrowd](https://www.fulfilmentcrowd.com/)
* [ChinaDivision](https://www.chinadivision.com/)
* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
[#computervision](https://www.linkedin.com/feed/hashtag/?keywords=computervision&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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[#c](https://www.linkedin.com/feed/hashtag/?keywords=c&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)++
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
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* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
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#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
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* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
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* [How to start](/topics-and-projects/how-to-start)
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* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
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* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
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* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
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[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
[#computervision](https://www.linkedin.com/feed/hashtag/?keywords=computervision&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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[#c](https://www.linkedin.com/feed/hashtag/?keywords=c&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)++
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#pirahansiah.com #farshid #pirahansiah
[#MultiCameraMultiClassMultiObjectTracking](https://www.linkedin.com/feed/hashtag/?keywords=multicameramulticlassmultiobjecttracking&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
[#deeplearning](https://www.linkedin.com/feed/hashtag/?keywords=deeplearning&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
* [fulfillmentcrowd](https://www.fulfilmentcrowd.com/)
* [ChinaDivision](https://www.chinadivision.com/)
* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
[#computervision](https://www.linkedin.com/feed/hashtag/?keywords=computervision&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
* [fulfillmentcrowd](https://www.fulfilmentcrowd.com/)
* [ChinaDivision](https://www.chinadivision.com/)
* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
* [fulfillmentcrowd](https://www.fulfilmentcrowd.com/)
* [ChinaDivision](https://www.chinadivision.com/)
* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
[#computervision](https://www.linkedin.com/feed/hashtag/?keywords=computervision&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
* [fulfillmentcrowd](https://www.fulfilmentcrowd.com/)
* [ChinaDivision](https://www.chinadivision.com/)
* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
[#computervision](https://www.linkedin.com/feed/hashtag/?keywords=computervision&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536)
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
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* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# Hardware
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
Hardware for Deep Learning (machine learning)
My experience
Raspberry Pi 4
Smart AI IoT, Robotic, 3D SLAM, AR, VR
RISC-V
I worked with many different hardware such as
Camera
What is important?
Scaled-YOLOv4:scaling model based on hardware
Cost
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
Use special frameworks or library for edge devices:
In some case you need to enhance model for inference. There are many
techniques to use such as,
How
#
Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera
* Camera
* * Camera Specs: Color camera, Stereo pair
* [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013)
* DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58°
* Resolution: 13MP (4208x3120), 480P (640x480)
* Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞
* Max Framerate: 35 FPS, 120 FPS
* Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g
* chips:
* * Robotics Vision Core 2 (RVC2 in short)
Myriad X are integrated into the Robotics Vision Core 2
* Speed ML
* * Model name, Size, FPS, Latency [ms],
* MobileOne S0 224x224, 165.5, 11.1
* YoloV8n, 416x416, 31.3, 56.9,
* YoloV8n, 640x640, 14.3, 123.6
* YoloV8s, 416x416, 15.2, 111.9
* YoloV8m, 416x416, 6.0, 273.8
#
Hardware for Deep Learning (machine learning)
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
I experiment with many different hardware to train and run deep learning
application. The below list shows my suggestion, comparison, expectation of
using different hardware. Embedded AI, implementing distributed data parallel,
distributed model parallel solutions.
[https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware)
#hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah
Laptop:
* NVIDIA Geforce RTX 3080 Ti
* Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,
Desktop
* eGPU
* Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU
* Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU
* GPU
* Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce
* MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro
IoT:
* Raspberry pi 3 (you need accelerator )
* Raspberry pi 4 (you need accelerator )
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano ( 2GB and 4GB ram)
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* NVIDIA AGX Orin = ~ 1900 Euro
* [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/)
* OpenCV AI Kit
* OAK = ~ 100 Euro
* OAK—D = ~ 200 Euro
* OAK—D + Wifi = ~ 250 Euro
* OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro
* OAK—D lite = ~ 100 Euro
#
My experience
I tested many different hardware for different computer vision applications in
area of IoT and Robotics
AI Edge: How to inference deep learning models on edge/IoT ; Enabling
efficient high-performance ; Accelerators/Optimization on Deep Learning
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#imageprocessing #patternrecognition
#
Raspberry Pi 4
How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to
install and boot from USB 3 (SSD)
1. update Raspberry Pi 4 EEPROM boot recovery
2. install Ubuntu 20 on SSD
3. change the config.txt and add "program_usb_boot_mode=1" at the end of file
4. remove and micro sd card and boot from ssd
#
Smart AI IoT, Robotic, 3D SLAM, AR, VR
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
#
[RISC-V](/workshops-and-events/risc-v)
#
I worked with many different hardware such as
* Raspberry pi 3
* Raspberry pi 4
* Intel® Neural Compute Stick 2
* Intel® Distribution of OpenVINO™ Toolkit
* I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models
* Google Coral
* I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models
* Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used
* NVIDIA Jetson Nano
* I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes
* NVIDIA JETSON AGX XAVIER
* The best hardware
* I attended in may conferences and summits in area of Hardware for deep learning such as:
* * * AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* RISC-V Summit (December 2020)
* OpenCV AI Kit
##
Camera
I worked with many different cameras such as:
* Camera Module V1
* Camera Module V2
* Camera Module V2.1
* multispectral camera
* USB webcam
* IP camera
* high resolution camera > 8K
* depth camera
* stereo camera
###
What is important?
* camera calibration is important
* Quantum efficiency [%] (spectral response)
* Sensor size [inches or mm] and pixel size [micro meter]
* Dynamic Range [dB]
* Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance
* inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play
* * firewire, 4.5 , 64, *, *, **, **
* gige, 100, 100, **, **, *, *
* usb, 8, 350, *, *, **, **
* link, 10, 850, -, -, **, -
* usb-c, 10, 40 GB,,,,
* distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length,
* * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature )
* some online tools: baslerweb.com, edmundoptics.com, flir.com
* to sum up
* use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues
* find your best trade-off between WD and FOV
* sometimes you cannot have everything in life!
* your lens aperture (f/#) is your friend, use it!
* a larger DOF requires a larger f/#
* lens performance curves are the ultimate documentation to read when selecting a lens
* understanding them properly requires good knowledge in optics, but it totally worth it.
##
Scaled-YOLOv4:scaling model based on hardware
#
Cost
* [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html)
* [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor)
* Hardware
* NVIDIA Jetson Xavier NX Developer Kit
* WIFI
* SparkFun GPS-RTK Dead Reckoning pHAT
* Micro Sd card
* Mophie Powerstation USB C 20000
* ZED 2 Stereo Camera
* 3D-printed box
* AWS
* AWS S3
* AWS xml.p2.xlarge EC2 instances
* AWS Sagemaker
* [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2)
* [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2)
* Post Product to customer by
* * * * * * * [easyship](https://www.easyship.com/)
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* [ChinaDivision](https://www.chinadivision.com/)
* [ORQA FPV](https://orqafpv.com/)
* [floship](https://www.floship.com/)
Update 26.April.2021
#
How to use computer vision with deep learning in IoT devices. Inference
machine learning on Edge require some extra steps.
I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel®
Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc.
Different OS: real-time operating system (RTOS), Nasa cFS (core Flight
System), Real-Time Executive for Multiprocessor Systems (RTEMS),
anomaly detection, object detection, object tracking, ...
##
Use special frameworks or library for edge devices:
* NVIDIA TensorRT
* TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com
* TensorFlow.js
* PyTorch Lightning
* PyTorch Mobile
* Intel® Distribution of OpenVINO Toolkit
* CoreML
* ML kit
* FRITZ
* MediaPipe
* Apache TVM
* TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino
* Libraries: ffmpeg, GStreamer, celery,
* GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy
Moreover, think about deep learning model for your specific hardware at first
stage.
##
In some case you need to enhance model for inference. There are many
techniques to use such as,
* Pruning
* Quantization
* Distillation Techniques
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Distributed machine learning and load balancing strategy
* Low rank matrix factorization (LRMF)
* Compact convolutional filters (Video/CNN)
* Knowledge distillation
* Neural Networks Compression Framework (NNCF)
* Parallel programming
##
How
Distributed machine learning and load balancing strategy
Pruning
model pruning: reducing redundant parameters which are not sensitive to the
performance. aim: remove all connections with absolute weights below a
threshold. 🤔go for bigger size of network with many layers then pruning much
better and faster
Quantization
The best way is using Google library which support most comprehensive methods
compresses by reducing the number of bits used to represent the weights
quantization effectively constraints the number of different weights we can
use inside our kernels per channel quantization for weights, which improves
performance by model compression and latency reduction.
training a compact neural network with distilled knowledge of a large model
distillation (knowledge transfer) from an ensemble of big networks into a much
smaller network which learns directly from the cumbersome model's outputs,
that is lighter to deploy
Distillation Techniques
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural
Networks for Resource-Constrained IoT Platforms
Binarized Neural Networks (BNNs)
It is not support by GPU hardware such as Jetson Nano. mostly based on CPU
Apache TVM (incubating) is a compiler stack for deep learning systems
challenges with large scale models deep neural networks are: expensive
computationally expensive memory intensive hindering their deployment
in:devices with low memory resources applications with strict latency
requirements other issues:data security: tend to memorize everything including
PII bias e.g. profanity: trained on large scale public datas elf discovering:
instead of manually configuring conversational flows, automatically discover
them from your data self training: let your system train itself with new
example s self managing: let your system optimize by itself knowledge
distillation
Distributed machine learning and load balancing strategy
run models which use all processing power like CPU,GPU,DSP,AI chip together to
enhance inference performance. dynamic pruning of kernels which aims to the
parsimonious inference by learning to exploit and dynamically remove the
redundant capacity of a CNN architecture. partitioning techniques through
convolution layer fusion to dynamically select the optimal partition according
to the availability of computational resources and network conditions.
Low rank matrix factorization (LRMF)
there exists latent structures in the data, by uncovering which we can obtain
a compressed representation of the dataLRMF factorizes the original matrix
into lower rank matrices while preserving latent structures and addressing the
issue of sparseness
Compact convolutional filters (Video/CNN)
designing special structural convolutional filters to save parameters replace
over parametric filters with compact filters to achieve overall speedup while
maintaining comparable accuracy
Knowledge distillation
Neural Networks Compression Framework (NNCF)
AI Edge: How to inference deep learning models on edge/IoT Enabling efficient
high-performance Accelerators/Optimization on Deep Learning
if the object is large and we do not need small anchor
in mobileNet we can remove small part of network which related to small
objects. in YOLO reduce number of anchor. decrease size of image input but
reduce the accuracy
Parallel programming and clean code, design pattern,
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# My paper: A Comprehensive Review on Deep Reinforcement Learning
The updates
2021
YouTube
Notes and info
Links:
Reading List (Video, Conference, Workshop, Paper)
###
The updates
Dear friends,
I recently wrote a survey paper on "A Comprehensive Review on Deep
Reinforcement Learning: A Survey", with some of the leading AI and DRL
researchers (including): In this work, we covered top recent DRL works,
grouped into several categories. We were lucky to have you, as the external
reviewers of this work. I hope this is useful for the research community. Any
feedback will be highly welcomed. You can find its summary here too. Imitation
learning, expert (teacher), hierarchical, hybrid imitation, high performance
parallelism,
#
2021
* [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo)
* [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2)
###
YouTube
* Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE)
* Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0)
* Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU)
* Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4)
* Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0)
* How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o)
* Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA)
* Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4)
* **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG)
###
Notes and info
* training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world
* Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive.
* using reinforcement learning to train robots that reason about how their actions will affect their environment.
* How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.
* In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video?
* One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon.
* Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI.
* DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design
###
Links:
* [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U)
###
Reading List (Video, Conference, Workshop, Paper)
* [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4)
DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For
Machine Learning
* Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)
* Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)
* Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)
Google to release DeepMind's StreetLearn for teaching machine-learning agents
to navigate cities
[https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn-
for-teaching-machine-learning-agents-to-navigate-
cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle-
to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to-
navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI)
Scalable agent alignment via reward modeling – DeepMind Safety Research –
Medium
[https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via-
reward-modeling-
bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable-
agent-alignment-via-reward-modeling-
bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR)
Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech
[https://www.extremetech.com/extreme/292409-googles-deepmind-can-support-
defeat-human-players-in-quake-iii-
arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles-
deepmind-can-support-defeat-human-players-in-quake-iii-
arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy)
[https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-)
| You searched for deep mind - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai-
moves-board-games-starcraft-
ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II -
ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges
Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-
unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google-
deepmind-ai-
unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's
DeepMind AI gets a few new tricks to learn faster[
](https://www.youtube.com/results)?
Robot arm
**There are 4 Courses in this Specialization**
**Course** 1
[ **Fundamentals of Reinforcement
Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals-
of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm)
4.8
stars
801 ratings
•
205 reviews
Reinforcement Learning is a subfield of Machine Learning, but is also a
general purpose formalism for automated decision-making and AI. This course
introduces you to statistical learning techniques where an agent explicitly
takes actions and interacts with the world. Understanding the importance and
challenges of learning agents that make decisions is of vital importance
today, with more and more companies interested in interactive agents and
intelligent decision-making.
This course introduces you to the fundamentals of Reinforcement Learning. When
you finish this course, you will: - Formalize problems as Markov Decision
Processes - Understand basic exploration methods and the
exploration/exploitation tradeoff - Understand value functions, as a general-
purpose tool for optimal decision-making - Know how to implement dynamic
programming as an efficient solution approach to an industrial control problem
This course teaches you the key concepts of Reinforcement Learning, underlying
classic and modern algorithms in RL. After completing this course, you will be
able to start using RL for real problems, where you have or can specify the
MDP. This is the first course of the Reinforcement Learning Specialization.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 2
[ **Sample-based Learning
Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample-
based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ)
4.8
stars
397 ratings
•
75 reviews
In this course, you will learn about several algorithms that can learn near
optimal policies based on trial and error interaction with the environment---
learning from the agent’s own experience. Learning from actual experience is
striking because it requires no prior knowledge of the environment’s dynamics,
yet can still attain optimal behavior. We will cover intuitively simple but
powerful Monte Carlo methods, and temporal difference learning methods
including Q-learning. We will wrap up this course investigating how we can get
the best of both worlds: algorithms that can combine model-based planning
(similar to dynamic programming) and temporal difference updates to radically
accelerate learning.
By the end of this course you will be able to: - Understand Temporal-
Difference learning and Monte Carlo as two strategies for estimating value
functions from sampled experience - Understand the importance of exploration,
when using sampled experience rather than dynamic programming sweeps within a
model - Understand the connections between Monte Carlo and Dynamic Programming
and TD. - Implement and apply the TD algorithm, for estimating value functions
- Implement and apply Expected Sarsa and Q-learning (two TD methods for
control) - Understand the difference between on-policy and off-policy control
- Understand planning with simulated experience (as opposed to classic
planning strategies) - Implement a model-based approach to RL, called Dyna,
which uses simulated experience - Conduct an empirical study to see the
improvements in sample efficiency when using Dyna
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 3
[ **Prediction and Control with Function
Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction-
control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH)
4.8
stars
252 ratings
•
40 reviews
In this course, you will learn how to solve problems with large, high-
dimensional, and potentially infinite state spaces. You will see that
estimating value functions can be cast as a supervised learning problem---
function approximation---allowing you to build agents that carefully balance
generalization and discrimination in order to maximize reward. We will begin
this journey by investigating how our policy evaluation or prediction methods
like Monte Carlo and TD can be extended to the function approximation setting.
You will learn about feature construction techniques for RL, and
representation learning via neural networks and backprop. We conclude this
course with a deep-dive into policy gradient methods; a way to learn policies
directly without learning a value function. In this course you will solve two
continuous-state control tasks and investigate the benefits of policy gradient
methods in a continuous-action environment.
Prerequisites: This course strongly builds on the fundamentals of Courses 1
and 2, and learners should have completed these before starting this course.
Learners should also be comfortable with probabilities & expectations, basic
linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing
algorithms from pseudocode. By the end of this course, you will be able to:
-Understand how to use supervised learning approaches to approximate value
functions -Understand objectives for prediction (value estimation) under
function approximation -Implement TD with function approximation (state
aggregation), on an environment with an infinite state space (continuous state
space) -Understand fixed basis and neural network approaches to feature
construction -Implement TD with neural network function approximation in a
continuous state environment -Understand new difficulties in exploration when
moving to function approximation -Contrast discounted problem formulations for
control versus an average reward problem formulation -Implement expected Sarsa
and Q-learning with function approximation on a continuous state control task
-Understand objectives for directly estimating policies (policy gradient
objectives) -Implement a policy gradient method (called Actor-Critic) on a
discrete state environment
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 4
[ **A Complete Reinforcement Learning System
(Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete-
reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm)
4.6
stars
177 ratings
•
33 reviews
In this final course, you will put together your knowledge from Courses 1, 2
and 3 to implement a complete RL solution to a problem. This capstone will let
you see how each component---problem formulation, algorithm selection,
parameter selection and representation design---fits together into a complete
solution, and how to make appropriate choices when deploying RL in the real
world. This project will require you to implement both the environment to
stimulate your problem, and a control agent with Neural Network function
approximation. In addition, you will conduct a scientific study of your
learning system to develop your ability to assess the robustness of RL agents.
To use RL in the real world, it is critical to (a) appropriately formalize the
problem as an MDP, (b) select appropriate algorithms, (c ) identify what
choices in your implementation will have large impacts on performance and (d)
validate the expected behaviour of your algorithms. This capstone is valuable
for anyone who is planning on using RL to solve real problems.
To be successful in this course, you will need to have completed Courses 1, 2,
and 3 of this Specialization or the equivalent. By the end of this course, you
will be able to: Complete an RL solution to a problem, starting from problem
formulation, appropriate algorithm selection and implementation and empirical
study into the effectiveness of the solution.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Using pre trained model to train deeper and lager model**
Imitation Learning
Safety Gym, a suite of environments and tools for measuring progress towards
reinforcement learning agents that respect safety constraints while training.
It also provides a standardized method of comparing algorithms and how well
they avoid costly mistakes while learning. If deep reinforcement learning is
applied to the real world, whether in robotics or internet-based tasks, it
will be important to have algorithms that are safe even while learning—like a
self-driving car that can learn to avoid accidents without actually having to
experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/
Datascience posts:[
](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)
[OpenAI Safety Gym: A Safe Place For AIs To Learn
💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo)
**DeepMind proposes novel way to train ‘safe’ reinforcement learning AI**
[https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe-
reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind-
proposes-novel-way-to-train-safe-reinforcement-learning-
ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ-
HA8tJ)
**The Batch** Issue 35
**Different Skills From Different Demos**
Reinforcement learning trains models by trial and error. In batch
reinforcement learning (BRL), models learn by observing many demonstrations by
a variety of actors. For instance, a robot might learn how to fix ingrown
toenails by watching hundreds of surgeons perform the procedure. But what if
one doctor is handier with a scalpel while another excels at suturing? A new
method lets models absorb the best skills from each.
**What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the
University of Toronto devised a BRL technique that enables models to learn
different portions of a task from different examples. This way, the model can
gain useful information from inconsistent examples.[
](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit
Reinforcement without Interaction at
Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)
(IRIS) achieved state-of-the-art BRL performance in three tasks performed in a
virtual environment.
**Key insight:** Learning from demonstrations is a double-edged sword. An
agent gets to see how to complete a task, but the scope of its action is
limited to the most complete demonstration of a given task. IRIS breaks down
tasks into sequences of intermediate subgoals. Then it performs the actions
required to accomplish each subgoal. In this way, the agent learns from the
best parts of each demonstration and combines them to accomplish the task.
**How it works:** IRIS includes a subgoal selection model that predicts
intermediate points on the way to accomplishing an assigned task. These
subgoals are defined automatically by the algorithm, and may not correspond to
parts of a task as humans would describe them. A controller network tries to
replicate the optimal sequence of actions leading to a given subgoal.
* The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward.
* The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations.
* Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on.
**Results:** In the Robosuite's lifting and pick-and-place tasks, previous
state-of-the-art BRL approaches couldn't pick up objects reliably, nor place
them elsewhere at all. IRIS learned to pick up objects with over 80 percent
success and placed them with 30 percent success.
**Why it matters:** Automatically identifying subgoals has been a holy grail
in reinforcement learning, with active research in hierarchical RL and other
areas. The method used in this paper applies to relatively simple tasks where
things happen in a predictable sequence (such as picking and then placing),
but might be a small step in an important direction.
**We’re thinking:** Batch reinforcement learning is useful when a model must
be interpretable or safe — after all, a robotic surgeon shouldn’t experiment
on living patients — but it hasn’t been terribly effective. IRIS could make it
a viable option.
Dec 11, 2019
Issue 34
**Seeing the World Blindfolded**
In reinforcement learning, if researchers want an agent to have an internal
representation of its environment, they’ll build and train a world model that
it can refer to. New research shows that world models can emerge from standard
training, rather than needing to be built separately.
**What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and
David Ha enabled an agent to build a world model by blindfolding it as it
learned to accomplish tasks. They call their approach[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational
dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ).
**Key insight:** Blocking an agent's observations of the world at random
moments forces it to generate its own internal representation to fill in the
gaps. The agent learns this representation without being instructed to predict
how the environment will change in response to its actions.
**How it works:** At every timestep, the agent acts on either its observation
(framed in red in the video above) or its prediction of what it wasn’t able to
observe (imagery not framed in red). The agent contains a controller that
decides on the most rewarding action. To compute the potential reward of a
given action, the agent includes an additional deep net trained using the RL
algorithm REINFORCE.
* Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation.
* If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one.
* This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model.
**Results:** Observational dropout solved the task known as[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW),
in which the model must balance a pole upright on a rolling cart, even when
its view of the world was blocked 90 percent of the time. In a more complex[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car
Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)
task, in which a model must navigate a car around a track as fast as possible,
the model performed almost equally well whether it was allowed to see its
surroundings or blindfolded up to 60 percent of the time.
**Why it matters:** Modeling reality is often part art and part science.
World models generated by observational dropout aren’t perfect
representations, but they’re sufficient for some tasks. This work could lead
to simple-but-effective world models of complex environments that are
impractical to model completely.
**We’re thinking:** Technology being imperfect, observational dropout is a
fact of life, not just a research technique. A self-driving car or auto-
piloted airplane reliant on sensors that drop data points could create a
catastrophe. This technique could make high-stakes RL models more robust.
Dec 4, 2019
Issue 33
**Is AI Making Mastery Obsolete?**
Is there any reason to continue playing games that AI has mastered? Ask the
former champions who have been toppled by machines.
**What happened:** In 2016, International Go master Lee Sedol famously lost
three out of four matches to DeepMind’s AlphaGo model. The 36-year-old
announced his retirement from competition on November 27. “Even if I become
the number one, there is an entity that cannot be defeated,” he[
](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)
South Korean's Yonhap News Agency,
**Stages of grief:** Prior to the tournament, Lee predicted that he would
defeat AlphaGo easily. But the model’s inexplicable — and indefatigable —
playing style pushed him into fits of[
](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and
disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[
](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the
South Korean public.
**Reaching acceptance:** Garry Kasparov, the former world-champion chess
player, went through his own cycle of grief after being defeated by IBM’s
DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s
engineers of cheating. He later retracted the charge, and in 2017 wrote a
book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if
humans can overcome their feelings of being threatened by AI, they can learn
from it. The book advocates an augmented intelligence in which humans and
machines work together to solve problems.
**The human element:** Although AlphaGo won in the 2016 duel, its human
opponent still managed to shine. During the fourth match, Sedol made a[
](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it
defied AlphaGo’s expectation and led to his sole victory.
**We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by
a machine built by humans under the direction of AlphaGo research lead David
Silver. Human mastery is obsolete only if you ignore people like Silver and
his team.
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# My paper: A Comprehensive Review on Deep Reinforcement Learning
The updates
2021
YouTube
Notes and info
Links:
Reading List (Video, Conference, Workshop, Paper)
###
The updates
Dear friends,
I recently wrote a survey paper on "A Comprehensive Review on Deep
Reinforcement Learning: A Survey", with some of the leading AI and DRL
researchers (including): In this work, we covered top recent DRL works,
grouped into several categories. We were lucky to have you, as the external
reviewers of this work. I hope this is useful for the research community. Any
feedback will be highly welcomed. You can find its summary here too. Imitation
learning, expert (teacher), hierarchical, hybrid imitation, high performance
parallelism,
#
2021
* [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo)
* [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2)
###
YouTube
* Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE)
* Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0)
* Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU)
* Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4)
* Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0)
* How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o)
* Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA)
* Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4)
* **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG)
###
Notes and info
* training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world
* Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive.
* using reinforcement learning to train robots that reason about how their actions will affect their environment.
* How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.
* In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video?
* One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon.
* Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI.
* DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design
###
Links:
* [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U)
###
Reading List (Video, Conference, Workshop, Paper)
* [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4)
DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For
Machine Learning
* Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)
* Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)
* Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)
Google to release DeepMind's StreetLearn for teaching machine-learning agents
to navigate cities
[https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn-
for-teaching-machine-learning-agents-to-navigate-
cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle-
to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to-
navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI)
Scalable agent alignment via reward modeling – DeepMind Safety Research –
Medium
[https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via-
reward-modeling-
bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable-
agent-alignment-via-reward-modeling-
bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR)
Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech
[https://www.extremetech.com/extreme/292409-googles-deepmind-can-support-
defeat-human-players-in-quake-iii-
arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles-
deepmind-can-support-defeat-human-players-in-quake-iii-
arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy)
[https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-)
| You searched for deep mind - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai-
moves-board-games-starcraft-
ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II -
ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges
Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-
unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google-
deepmind-ai-
unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's
DeepMind AI gets a few new tricks to learn faster[
](https://www.youtube.com/results)?
Robot arm
**There are 4 Courses in this Specialization**
**Course** 1
[ **Fundamentals of Reinforcement
Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals-
of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm)
4.8
stars
801 ratings
•
205 reviews
Reinforcement Learning is a subfield of Machine Learning, but is also a
general purpose formalism for automated decision-making and AI. This course
introduces you to statistical learning techniques where an agent explicitly
takes actions and interacts with the world. Understanding the importance and
challenges of learning agents that make decisions is of vital importance
today, with more and more companies interested in interactive agents and
intelligent decision-making.
This course introduces you to the fundamentals of Reinforcement Learning. When
you finish this course, you will: - Formalize problems as Markov Decision
Processes - Understand basic exploration methods and the
exploration/exploitation tradeoff - Understand value functions, as a general-
purpose tool for optimal decision-making - Know how to implement dynamic
programming as an efficient solution approach to an industrial control problem
This course teaches you the key concepts of Reinforcement Learning, underlying
classic and modern algorithms in RL. After completing this course, you will be
able to start using RL for real problems, where you have or can specify the
MDP. This is the first course of the Reinforcement Learning Specialization.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 2
[ **Sample-based Learning
Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample-
based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ)
4.8
stars
397 ratings
•
75 reviews
In this course, you will learn about several algorithms that can learn near
optimal policies based on trial and error interaction with the environment---
learning from the agent’s own experience. Learning from actual experience is
striking because it requires no prior knowledge of the environment’s dynamics,
yet can still attain optimal behavior. We will cover intuitively simple but
powerful Monte Carlo methods, and temporal difference learning methods
including Q-learning. We will wrap up this course investigating how we can get
the best of both worlds: algorithms that can combine model-based planning
(similar to dynamic programming) and temporal difference updates to radically
accelerate learning.
By the end of this course you will be able to: - Understand Temporal-
Difference learning and Monte Carlo as two strategies for estimating value
functions from sampled experience - Understand the importance of exploration,
when using sampled experience rather than dynamic programming sweeps within a
model - Understand the connections between Monte Carlo and Dynamic Programming
and TD. - Implement and apply the TD algorithm, for estimating value functions
- Implement and apply Expected Sarsa and Q-learning (two TD methods for
control) - Understand the difference between on-policy and off-policy control
- Understand planning with simulated experience (as opposed to classic
planning strategies) - Implement a model-based approach to RL, called Dyna,
which uses simulated experience - Conduct an empirical study to see the
improvements in sample efficiency when using Dyna
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 3
[ **Prediction and Control with Function
Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction-
control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH)
4.8
stars
252 ratings
•
40 reviews
In this course, you will learn how to solve problems with large, high-
dimensional, and potentially infinite state spaces. You will see that
estimating value functions can be cast as a supervised learning problem---
function approximation---allowing you to build agents that carefully balance
generalization and discrimination in order to maximize reward. We will begin
this journey by investigating how our policy evaluation or prediction methods
like Monte Carlo and TD can be extended to the function approximation setting.
You will learn about feature construction techniques for RL, and
representation learning via neural networks and backprop. We conclude this
course with a deep-dive into policy gradient methods; a way to learn policies
directly without learning a value function. In this course you will solve two
continuous-state control tasks and investigate the benefits of policy gradient
methods in a continuous-action environment.
Prerequisites: This course strongly builds on the fundamentals of Courses 1
and 2, and learners should have completed these before starting this course.
Learners should also be comfortable with probabilities & expectations, basic
linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing
algorithms from pseudocode. By the end of this course, you will be able to:
-Understand how to use supervised learning approaches to approximate value
functions -Understand objectives for prediction (value estimation) under
function approximation -Implement TD with function approximation (state
aggregation), on an environment with an infinite state space (continuous state
space) -Understand fixed basis and neural network approaches to feature
construction -Implement TD with neural network function approximation in a
continuous state environment -Understand new difficulties in exploration when
moving to function approximation -Contrast discounted problem formulations for
control versus an average reward problem formulation -Implement expected Sarsa
and Q-learning with function approximation on a continuous state control task
-Understand objectives for directly estimating policies (policy gradient
objectives) -Implement a policy gradient method (called Actor-Critic) on a
discrete state environment
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 4
[ **A Complete Reinforcement Learning System
(Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete-
reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm)
4.6
stars
177 ratings
•
33 reviews
In this final course, you will put together your knowledge from Courses 1, 2
and 3 to implement a complete RL solution to a problem. This capstone will let
you see how each component---problem formulation, algorithm selection,
parameter selection and representation design---fits together into a complete
solution, and how to make appropriate choices when deploying RL in the real
world. This project will require you to implement both the environment to
stimulate your problem, and a control agent with Neural Network function
approximation. In addition, you will conduct a scientific study of your
learning system to develop your ability to assess the robustness of RL agents.
To use RL in the real world, it is critical to (a) appropriately formalize the
problem as an MDP, (b) select appropriate algorithms, (c ) identify what
choices in your implementation will have large impacts on performance and (d)
validate the expected behaviour of your algorithms. This capstone is valuable
for anyone who is planning on using RL to solve real problems.
To be successful in this course, you will need to have completed Courses 1, 2,
and 3 of this Specialization or the equivalent. By the end of this course, you
will be able to: Complete an RL solution to a problem, starting from problem
formulation, appropriate algorithm selection and implementation and empirical
study into the effectiveness of the solution.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Using pre trained model to train deeper and lager model**
Imitation Learning
Safety Gym, a suite of environments and tools for measuring progress towards
reinforcement learning agents that respect safety constraints while training.
It also provides a standardized method of comparing algorithms and how well
they avoid costly mistakes while learning. If deep reinforcement learning is
applied to the real world, whether in robotics or internet-based tasks, it
will be important to have algorithms that are safe even while learning—like a
self-driving car that can learn to avoid accidents without actually having to
experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/
Datascience posts:[
](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)
[OpenAI Safety Gym: A Safe Place For AIs To Learn
💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo)
**DeepMind proposes novel way to train ‘safe’ reinforcement learning AI**
[https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe-
reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind-
proposes-novel-way-to-train-safe-reinforcement-learning-
ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ-
HA8tJ)
**The Batch** Issue 35
**Different Skills From Different Demos**
Reinforcement learning trains models by trial and error. In batch
reinforcement learning (BRL), models learn by observing many demonstrations by
a variety of actors. For instance, a robot might learn how to fix ingrown
toenails by watching hundreds of surgeons perform the procedure. But what if
one doctor is handier with a scalpel while another excels at suturing? A new
method lets models absorb the best skills from each.
**What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the
University of Toronto devised a BRL technique that enables models to learn
different portions of a task from different examples. This way, the model can
gain useful information from inconsistent examples.[
](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit
Reinforcement without Interaction at
Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)
(IRIS) achieved state-of-the-art BRL performance in three tasks performed in a
virtual environment.
**Key insight:** Learning from demonstrations is a double-edged sword. An
agent gets to see how to complete a task, but the scope of its action is
limited to the most complete demonstration of a given task. IRIS breaks down
tasks into sequences of intermediate subgoals. Then it performs the actions
required to accomplish each subgoal. In this way, the agent learns from the
best parts of each demonstration and combines them to accomplish the task.
**How it works:** IRIS includes a subgoal selection model that predicts
intermediate points on the way to accomplishing an assigned task. These
subgoals are defined automatically by the algorithm, and may not correspond to
parts of a task as humans would describe them. A controller network tries to
replicate the optimal sequence of actions leading to a given subgoal.
* The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward.
* The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations.
* Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on.
**Results:** In the Robosuite's lifting and pick-and-place tasks, previous
state-of-the-art BRL approaches couldn't pick up objects reliably, nor place
them elsewhere at all. IRIS learned to pick up objects with over 80 percent
success and placed them with 30 percent success.
**Why it matters:** Automatically identifying subgoals has been a holy grail
in reinforcement learning, with active research in hierarchical RL and other
areas. The method used in this paper applies to relatively simple tasks where
things happen in a predictable sequence (such as picking and then placing),
but might be a small step in an important direction.
**We’re thinking:** Batch reinforcement learning is useful when a model must
be interpretable or safe — after all, a robotic surgeon shouldn’t experiment
on living patients — but it hasn’t been terribly effective. IRIS could make it
a viable option.
Dec 11, 2019
Issue 34
**Seeing the World Blindfolded**
In reinforcement learning, if researchers want an agent to have an internal
representation of its environment, they’ll build and train a world model that
it can refer to. New research shows that world models can emerge from standard
training, rather than needing to be built separately.
**What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and
David Ha enabled an agent to build a world model by blindfolding it as it
learned to accomplish tasks. They call their approach[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational
dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ).
**Key insight:** Blocking an agent's observations of the world at random
moments forces it to generate its own internal representation to fill in the
gaps. The agent learns this representation without being instructed to predict
how the environment will change in response to its actions.
**How it works:** At every timestep, the agent acts on either its observation
(framed in red in the video above) or its prediction of what it wasn’t able to
observe (imagery not framed in red). The agent contains a controller that
decides on the most rewarding action. To compute the potential reward of a
given action, the agent includes an additional deep net trained using the RL
algorithm REINFORCE.
* Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation.
* If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one.
* This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model.
**Results:** Observational dropout solved the task known as[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW),
in which the model must balance a pole upright on a rolling cart, even when
its view of the world was blocked 90 percent of the time. In a more complex[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car
Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)
task, in which a model must navigate a car around a track as fast as possible,
the model performed almost equally well whether it was allowed to see its
surroundings or blindfolded up to 60 percent of the time.
**Why it matters:** Modeling reality is often part art and part science.
World models generated by observational dropout aren’t perfect
representations, but they’re sufficient for some tasks. This work could lead
to simple-but-effective world models of complex environments that are
impractical to model completely.
**We’re thinking:** Technology being imperfect, observational dropout is a
fact of life, not just a research technique. A self-driving car or auto-
piloted airplane reliant on sensors that drop data points could create a
catastrophe. This technique could make high-stakes RL models more robust.
Dec 4, 2019
Issue 33
**Is AI Making Mastery Obsolete?**
Is there any reason to continue playing games that AI has mastered? Ask the
former champions who have been toppled by machines.
**What happened:** In 2016, International Go master Lee Sedol famously lost
three out of four matches to DeepMind’s AlphaGo model. The 36-year-old
announced his retirement from competition on November 27. “Even if I become
the number one, there is an entity that cannot be defeated,” he[
](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)
South Korean's Yonhap News Agency,
**Stages of grief:** Prior to the tournament, Lee predicted that he would
defeat AlphaGo easily. But the model’s inexplicable — and indefatigable —
playing style pushed him into fits of[
](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and
disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[
](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the
South Korean public.
**Reaching acceptance:** Garry Kasparov, the former world-champion chess
player, went through his own cycle of grief after being defeated by IBM’s
DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s
engineers of cheating. He later retracted the charge, and in 2017 wrote a
book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if
humans can overcome their feelings of being threatened by AI, they can learn
from it. The book advocates an augmented intelligence in which humans and
machines work together to solve problems.
**The human element:** Although AlphaGo won in the 2016 duel, its human
opponent still managed to shine. During the fourth match, Sedol made a[
](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it
defied AlphaGo’s expectation and led to his sole victory.
**We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by
a machine built by humans under the direction of AlphaGo research lead David
Silver. Human mastery is obsolete only if you ignore people like Silver and
his team.
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# My paper: A Comprehensive Review on Deep Reinforcement Learning
The updates
2021
YouTube
Notes and info
Links:
Reading List (Video, Conference, Workshop, Paper)
###
The updates
Dear friends,
I recently wrote a survey paper on "A Comprehensive Review on Deep
Reinforcement Learning: A Survey", with some of the leading AI and DRL
researchers (including): In this work, we covered top recent DRL works,
grouped into several categories. We were lucky to have you, as the external
reviewers of this work. I hope this is useful for the research community. Any
feedback will be highly welcomed. You can find its summary here too. Imitation
learning, expert (teacher), hierarchical, hybrid imitation, high performance
parallelism,
#
2021
* [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo)
* [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2)
###
YouTube
* Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE)
* Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0)
* Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU)
* Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4)
* Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0)
* How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o)
* Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA)
* Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4)
* **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG)
###
Notes and info
* training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world
* Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive.
* using reinforcement learning to train robots that reason about how their actions will affect their environment.
* How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.
* In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video?
* One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon.
* Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI.
* DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design
###
Links:
* [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U)
###
Reading List (Video, Conference, Workshop, Paper)
* [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4)
DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For
Machine Learning
* Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)
* Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)
* Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)
Google to release DeepMind's StreetLearn for teaching machine-learning agents
to navigate cities
[https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn-
for-teaching-machine-learning-agents-to-navigate-
cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle-
to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to-
navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI)
Scalable agent alignment via reward modeling – DeepMind Safety Research –
Medium
[https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via-
reward-modeling-
bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable-
agent-alignment-via-reward-modeling-
bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR)
Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech
[https://www.extremetech.com/extreme/292409-googles-deepmind-can-support-
defeat-human-players-in-quake-iii-
arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles-
deepmind-can-support-defeat-human-players-in-quake-iii-
arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy)
[https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-)
| You searched for deep mind - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai-
moves-board-games-starcraft-
ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II -
ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges
Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-
unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google-
deepmind-ai-
unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's
DeepMind AI gets a few new tricks to learn faster[
](https://www.youtube.com/results)?
Robot arm
**There are 4 Courses in this Specialization**
**Course** 1
[ **Fundamentals of Reinforcement
Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals-
of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm)
4.8
stars
801 ratings
•
205 reviews
Reinforcement Learning is a subfield of Machine Learning, but is also a
general purpose formalism for automated decision-making and AI. This course
introduces you to statistical learning techniques where an agent explicitly
takes actions and interacts with the world. Understanding the importance and
challenges of learning agents that make decisions is of vital importance
today, with more and more companies interested in interactive agents and
intelligent decision-making.
This course introduces you to the fundamentals of Reinforcement Learning. When
you finish this course, you will: - Formalize problems as Markov Decision
Processes - Understand basic exploration methods and the
exploration/exploitation tradeoff - Understand value functions, as a general-
purpose tool for optimal decision-making - Know how to implement dynamic
programming as an efficient solution approach to an industrial control problem
This course teaches you the key concepts of Reinforcement Learning, underlying
classic and modern algorithms in RL. After completing this course, you will be
able to start using RL for real problems, where you have or can specify the
MDP. This is the first course of the Reinforcement Learning Specialization.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 2
[ **Sample-based Learning
Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample-
based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ)
4.8
stars
397 ratings
•
75 reviews
In this course, you will learn about several algorithms that can learn near
optimal policies based on trial and error interaction with the environment---
learning from the agent’s own experience. Learning from actual experience is
striking because it requires no prior knowledge of the environment’s dynamics,
yet can still attain optimal behavior. We will cover intuitively simple but
powerful Monte Carlo methods, and temporal difference learning methods
including Q-learning. We will wrap up this course investigating how we can get
the best of both worlds: algorithms that can combine model-based planning
(similar to dynamic programming) and temporal difference updates to radically
accelerate learning.
By the end of this course you will be able to: - Understand Temporal-
Difference learning and Monte Carlo as two strategies for estimating value
functions from sampled experience - Understand the importance of exploration,
when using sampled experience rather than dynamic programming sweeps within a
model - Understand the connections between Monte Carlo and Dynamic Programming
and TD. - Implement and apply the TD algorithm, for estimating value functions
- Implement and apply Expected Sarsa and Q-learning (two TD methods for
control) - Understand the difference between on-policy and off-policy control
- Understand planning with simulated experience (as opposed to classic
planning strategies) - Implement a model-based approach to RL, called Dyna,
which uses simulated experience - Conduct an empirical study to see the
improvements in sample efficiency when using Dyna
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 3
[ **Prediction and Control with Function
Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction-
control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH)
4.8
stars
252 ratings
•
40 reviews
In this course, you will learn how to solve problems with large, high-
dimensional, and potentially infinite state spaces. You will see that
estimating value functions can be cast as a supervised learning problem---
function approximation---allowing you to build agents that carefully balance
generalization and discrimination in order to maximize reward. We will begin
this journey by investigating how our policy evaluation or prediction methods
like Monte Carlo and TD can be extended to the function approximation setting.
You will learn about feature construction techniques for RL, and
representation learning via neural networks and backprop. We conclude this
course with a deep-dive into policy gradient methods; a way to learn policies
directly without learning a value function. In this course you will solve two
continuous-state control tasks and investigate the benefits of policy gradient
methods in a continuous-action environment.
Prerequisites: This course strongly builds on the fundamentals of Courses 1
and 2, and learners should have completed these before starting this course.
Learners should also be comfortable with probabilities & expectations, basic
linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing
algorithms from pseudocode. By the end of this course, you will be able to:
-Understand how to use supervised learning approaches to approximate value
functions -Understand objectives for prediction (value estimation) under
function approximation -Implement TD with function approximation (state
aggregation), on an environment with an infinite state space (continuous state
space) -Understand fixed basis and neural network approaches to feature
construction -Implement TD with neural network function approximation in a
continuous state environment -Understand new difficulties in exploration when
moving to function approximation -Contrast discounted problem formulations for
control versus an average reward problem formulation -Implement expected Sarsa
and Q-learning with function approximation on a continuous state control task
-Understand objectives for directly estimating policies (policy gradient
objectives) -Implement a policy gradient method (called Actor-Critic) on a
discrete state environment
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 4
[ **A Complete Reinforcement Learning System
(Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete-
reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm)
4.6
stars
177 ratings
•
33 reviews
In this final course, you will put together your knowledge from Courses 1, 2
and 3 to implement a complete RL solution to a problem. This capstone will let
you see how each component---problem formulation, algorithm selection,
parameter selection and representation design---fits together into a complete
solution, and how to make appropriate choices when deploying RL in the real
world. This project will require you to implement both the environment to
stimulate your problem, and a control agent with Neural Network function
approximation. In addition, you will conduct a scientific study of your
learning system to develop your ability to assess the robustness of RL agents.
To use RL in the real world, it is critical to (a) appropriately formalize the
problem as an MDP, (b) select appropriate algorithms, (c ) identify what
choices in your implementation will have large impacts on performance and (d)
validate the expected behaviour of your algorithms. This capstone is valuable
for anyone who is planning on using RL to solve real problems.
To be successful in this course, you will need to have completed Courses 1, 2,
and 3 of this Specialization or the equivalent. By the end of this course, you
will be able to: Complete an RL solution to a problem, starting from problem
formulation, appropriate algorithm selection and implementation and empirical
study into the effectiveness of the solution.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Using pre trained model to train deeper and lager model**
Imitation Learning
Safety Gym, a suite of environments and tools for measuring progress towards
reinforcement learning agents that respect safety constraints while training.
It also provides a standardized method of comparing algorithms and how well
they avoid costly mistakes while learning. If deep reinforcement learning is
applied to the real world, whether in robotics or internet-based tasks, it
will be important to have algorithms that are safe even while learning—like a
self-driving car that can learn to avoid accidents without actually having to
experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/
Datascience posts:[
](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)
[OpenAI Safety Gym: A Safe Place For AIs To Learn
💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo)
**DeepMind proposes novel way to train ‘safe’ reinforcement learning AI**
[https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe-
reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind-
proposes-novel-way-to-train-safe-reinforcement-learning-
ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ-
HA8tJ)
**The Batch** Issue 35
**Different Skills From Different Demos**
Reinforcement learning trains models by trial and error. In batch
reinforcement learning (BRL), models learn by observing many demonstrations by
a variety of actors. For instance, a robot might learn how to fix ingrown
toenails by watching hundreds of surgeons perform the procedure. But what if
one doctor is handier with a scalpel while another excels at suturing? A new
method lets models absorb the best skills from each.
**What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the
University of Toronto devised a BRL technique that enables models to learn
different portions of a task from different examples. This way, the model can
gain useful information from inconsistent examples.[
](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit
Reinforcement without Interaction at
Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)
(IRIS) achieved state-of-the-art BRL performance in three tasks performed in a
virtual environment.
**Key insight:** Learning from demonstrations is a double-edged sword. An
agent gets to see how to complete a task, but the scope of its action is
limited to the most complete demonstration of a given task. IRIS breaks down
tasks into sequences of intermediate subgoals. Then it performs the actions
required to accomplish each subgoal. In this way, the agent learns from the
best parts of each demonstration and combines them to accomplish the task.
**How it works:** IRIS includes a subgoal selection model that predicts
intermediate points on the way to accomplishing an assigned task. These
subgoals are defined automatically by the algorithm, and may not correspond to
parts of a task as humans would describe them. A controller network tries to
replicate the optimal sequence of actions leading to a given subgoal.
* The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward.
* The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations.
* Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on.
**Results:** In the Robosuite's lifting and pick-and-place tasks, previous
state-of-the-art BRL approaches couldn't pick up objects reliably, nor place
them elsewhere at all. IRIS learned to pick up objects with over 80 percent
success and placed them with 30 percent success.
**Why it matters:** Automatically identifying subgoals has been a holy grail
in reinforcement learning, with active research in hierarchical RL and other
areas. The method used in this paper applies to relatively simple tasks where
things happen in a predictable sequence (such as picking and then placing),
but might be a small step in an important direction.
**We’re thinking:** Batch reinforcement learning is useful when a model must
be interpretable or safe — after all, a robotic surgeon shouldn’t experiment
on living patients — but it hasn’t been terribly effective. IRIS could make it
a viable option.
Dec 11, 2019
Issue 34
**Seeing the World Blindfolded**
In reinforcement learning, if researchers want an agent to have an internal
representation of its environment, they’ll build and train a world model that
it can refer to. New research shows that world models can emerge from standard
training, rather than needing to be built separately.
**What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and
David Ha enabled an agent to build a world model by blindfolding it as it
learned to accomplish tasks. They call their approach[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational
dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ).
**Key insight:** Blocking an agent's observations of the world at random
moments forces it to generate its own internal representation to fill in the
gaps. The agent learns this representation without being instructed to predict
how the environment will change in response to its actions.
**How it works:** At every timestep, the agent acts on either its observation
(framed in red in the video above) or its prediction of what it wasn’t able to
observe (imagery not framed in red). The agent contains a controller that
decides on the most rewarding action. To compute the potential reward of a
given action, the agent includes an additional deep net trained using the RL
algorithm REINFORCE.
* Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation.
* If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one.
* This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model.
**Results:** Observational dropout solved the task known as[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW),
in which the model must balance a pole upright on a rolling cart, even when
its view of the world was blocked 90 percent of the time. In a more complex[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car
Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)
task, in which a model must navigate a car around a track as fast as possible,
the model performed almost equally well whether it was allowed to see its
surroundings or blindfolded up to 60 percent of the time.
**Why it matters:** Modeling reality is often part art and part science.
World models generated by observational dropout aren’t perfect
representations, but they’re sufficient for some tasks. This work could lead
to simple-but-effective world models of complex environments that are
impractical to model completely.
**We’re thinking:** Technology being imperfect, observational dropout is a
fact of life, not just a research technique. A self-driving car or auto-
piloted airplane reliant on sensors that drop data points could create a
catastrophe. This technique could make high-stakes RL models more robust.
Dec 4, 2019
Issue 33
**Is AI Making Mastery Obsolete?**
Is there any reason to continue playing games that AI has mastered? Ask the
former champions who have been toppled by machines.
**What happened:** In 2016, International Go master Lee Sedol famously lost
three out of four matches to DeepMind’s AlphaGo model. The 36-year-old
announced his retirement from competition on November 27. “Even if I become
the number one, there is an entity that cannot be defeated,” he[
](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)
South Korean's Yonhap News Agency,
**Stages of grief:** Prior to the tournament, Lee predicted that he would
defeat AlphaGo easily. But the model’s inexplicable — and indefatigable —
playing style pushed him into fits of[
](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and
disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[
](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the
South Korean public.
**Reaching acceptance:** Garry Kasparov, the former world-champion chess
player, went through his own cycle of grief after being defeated by IBM’s
DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s
engineers of cheating. He later retracted the charge, and in 2017 wrote a
book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if
humans can overcome their feelings of being threatened by AI, they can learn
from it. The book advocates an augmented intelligence in which humans and
machines work together to solve problems.
**The human element:** Although AlphaGo won in the 2016 duel, its human
opponent still managed to shine. During the fourth match, Sedol made a[
](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it
defied AlphaGo’s expectation and led to his sole victory.
**We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by
a machine built by humans under the direction of AlphaGo research lead David
Silver. Human mastery is obsolete only if you ignore people like Silver and
his team.
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# My paper: A Comprehensive Review on Deep Reinforcement Learning
The updates
2021
YouTube
Notes and info
Links:
Reading List (Video, Conference, Workshop, Paper)
###
The updates
Dear friends,
I recently wrote a survey paper on "A Comprehensive Review on Deep
Reinforcement Learning: A Survey", with some of the leading AI and DRL
researchers (including): In this work, we covered top recent DRL works,
grouped into several categories. We were lucky to have you, as the external
reviewers of this work. I hope this is useful for the research community. Any
feedback will be highly welcomed. You can find its summary here too. Imitation
learning, expert (teacher), hierarchical, hybrid imitation, high performance
parallelism,
#
2021
* [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo)
* [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2)
###
YouTube
* Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE)
* Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0)
* Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU)
* Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4)
* Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0)
* How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o)
* Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA)
* Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4)
* **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG)
###
Notes and info
* training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world
* Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive.
* using reinforcement learning to train robots that reason about how their actions will affect their environment.
* How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.
* In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video?
* One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon.
* Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI.
* DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design
###
Links:
* [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U)
###
Reading List (Video, Conference, Workshop, Paper)
* [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4)
DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For
Machine Learning
* Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)
* Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)
* Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)
Google to release DeepMind's StreetLearn for teaching machine-learning agents
to navigate cities
[https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn-
for-teaching-machine-learning-agents-to-navigate-
cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle-
to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to-
navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI)
Scalable agent alignment via reward modeling – DeepMind Safety Research –
Medium
[https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via-
reward-modeling-
bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable-
agent-alignment-via-reward-modeling-
bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR)
Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech
[https://www.extremetech.com/extreme/292409-googles-deepmind-can-support-
defeat-human-players-in-quake-iii-
arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles-
deepmind-can-support-defeat-human-players-in-quake-iii-
arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy)
[https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-)
| You searched for deep mind - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai-
moves-board-games-starcraft-
ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II -
ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges
Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-
unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google-
deepmind-ai-
unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's
DeepMind AI gets a few new tricks to learn faster[
](https://www.youtube.com/results)?
Robot arm
**There are 4 Courses in this Specialization**
**Course** 1
[ **Fundamentals of Reinforcement
Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals-
of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm)
4.8
stars
801 ratings
•
205 reviews
Reinforcement Learning is a subfield of Machine Learning, but is also a
general purpose formalism for automated decision-making and AI. This course
introduces you to statistical learning techniques where an agent explicitly
takes actions and interacts with the world. Understanding the importance and
challenges of learning agents that make decisions is of vital importance
today, with more and more companies interested in interactive agents and
intelligent decision-making.
This course introduces you to the fundamentals of Reinforcement Learning. When
you finish this course, you will: - Formalize problems as Markov Decision
Processes - Understand basic exploration methods and the
exploration/exploitation tradeoff - Understand value functions, as a general-
purpose tool for optimal decision-making - Know how to implement dynamic
programming as an efficient solution approach to an industrial control problem
This course teaches you the key concepts of Reinforcement Learning, underlying
classic and modern algorithms in RL. After completing this course, you will be
able to start using RL for real problems, where you have or can specify the
MDP. This is the first course of the Reinforcement Learning Specialization.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 2
[ **Sample-based Learning
Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample-
based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ)
4.8
stars
397 ratings
•
75 reviews
In this course, you will learn about several algorithms that can learn near
optimal policies based on trial and error interaction with the environment---
learning from the agent’s own experience. Learning from actual experience is
striking because it requires no prior knowledge of the environment’s dynamics,
yet can still attain optimal behavior. We will cover intuitively simple but
powerful Monte Carlo methods, and temporal difference learning methods
including Q-learning. We will wrap up this course investigating how we can get
the best of both worlds: algorithms that can combine model-based planning
(similar to dynamic programming) and temporal difference updates to radically
accelerate learning.
By the end of this course you will be able to: - Understand Temporal-
Difference learning and Monte Carlo as two strategies for estimating value
functions from sampled experience - Understand the importance of exploration,
when using sampled experience rather than dynamic programming sweeps within a
model - Understand the connections between Monte Carlo and Dynamic Programming
and TD. - Implement and apply the TD algorithm, for estimating value functions
- Implement and apply Expected Sarsa and Q-learning (two TD methods for
control) - Understand the difference between on-policy and off-policy control
- Understand planning with simulated experience (as opposed to classic
planning strategies) - Implement a model-based approach to RL, called Dyna,
which uses simulated experience - Conduct an empirical study to see the
improvements in sample efficiency when using Dyna
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 3
[ **Prediction and Control with Function
Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction-
control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH)
4.8
stars
252 ratings
•
40 reviews
In this course, you will learn how to solve problems with large, high-
dimensional, and potentially infinite state spaces. You will see that
estimating value functions can be cast as a supervised learning problem---
function approximation---allowing you to build agents that carefully balance
generalization and discrimination in order to maximize reward. We will begin
this journey by investigating how our policy evaluation or prediction methods
like Monte Carlo and TD can be extended to the function approximation setting.
You will learn about feature construction techniques for RL, and
representation learning via neural networks and backprop. We conclude this
course with a deep-dive into policy gradient methods; a way to learn policies
directly without learning a value function. In this course you will solve two
continuous-state control tasks and investigate the benefits of policy gradient
methods in a continuous-action environment.
Prerequisites: This course strongly builds on the fundamentals of Courses 1
and 2, and learners should have completed these before starting this course.
Learners should also be comfortable with probabilities & expectations, basic
linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing
algorithms from pseudocode. By the end of this course, you will be able to:
-Understand how to use supervised learning approaches to approximate value
functions -Understand objectives for prediction (value estimation) under
function approximation -Implement TD with function approximation (state
aggregation), on an environment with an infinite state space (continuous state
space) -Understand fixed basis and neural network approaches to feature
construction -Implement TD with neural network function approximation in a
continuous state environment -Understand new difficulties in exploration when
moving to function approximation -Contrast discounted problem formulations for
control versus an average reward problem formulation -Implement expected Sarsa
and Q-learning with function approximation on a continuous state control task
-Understand objectives for directly estimating policies (policy gradient
objectives) -Implement a policy gradient method (called Actor-Critic) on a
discrete state environment
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 4
[ **A Complete Reinforcement Learning System
(Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete-
reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm)
4.6
stars
177 ratings
•
33 reviews
In this final course, you will put together your knowledge from Courses 1, 2
and 3 to implement a complete RL solution to a problem. This capstone will let
you see how each component---problem formulation, algorithm selection,
parameter selection and representation design---fits together into a complete
solution, and how to make appropriate choices when deploying RL in the real
world. This project will require you to implement both the environment to
stimulate your problem, and a control agent with Neural Network function
approximation. In addition, you will conduct a scientific study of your
learning system to develop your ability to assess the robustness of RL agents.
To use RL in the real world, it is critical to (a) appropriately formalize the
problem as an MDP, (b) select appropriate algorithms, (c ) identify what
choices in your implementation will have large impacts on performance and (d)
validate the expected behaviour of your algorithms. This capstone is valuable
for anyone who is planning on using RL to solve real problems.
To be successful in this course, you will need to have completed Courses 1, 2,
and 3 of this Specialization or the equivalent. By the end of this course, you
will be able to: Complete an RL solution to a problem, starting from problem
formulation, appropriate algorithm selection and implementation and empirical
study into the effectiveness of the solution.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Using pre trained model to train deeper and lager model**
Imitation Learning
Safety Gym, a suite of environments and tools for measuring progress towards
reinforcement learning agents that respect safety constraints while training.
It also provides a standardized method of comparing algorithms and how well
they avoid costly mistakes while learning. If deep reinforcement learning is
applied to the real world, whether in robotics or internet-based tasks, it
will be important to have algorithms that are safe even while learning—like a
self-driving car that can learn to avoid accidents without actually having to
experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/
Datascience posts:[
](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)
[OpenAI Safety Gym: A Safe Place For AIs To Learn
💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo)
**DeepMind proposes novel way to train ‘safe’ reinforcement learning AI**
[https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe-
reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind-
proposes-novel-way-to-train-safe-reinforcement-learning-
ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ-
HA8tJ)
**The Batch** Issue 35
**Different Skills From Different Demos**
Reinforcement learning trains models by trial and error. In batch
reinforcement learning (BRL), models learn by observing many demonstrations by
a variety of actors. For instance, a robot might learn how to fix ingrown
toenails by watching hundreds of surgeons perform the procedure. But what if
one doctor is handier with a scalpel while another excels at suturing? A new
method lets models absorb the best skills from each.
**What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the
University of Toronto devised a BRL technique that enables models to learn
different portions of a task from different examples. This way, the model can
gain useful information from inconsistent examples.[
](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit
Reinforcement without Interaction at
Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)
(IRIS) achieved state-of-the-art BRL performance in three tasks performed in a
virtual environment.
**Key insight:** Learning from demonstrations is a double-edged sword. An
agent gets to see how to complete a task, but the scope of its action is
limited to the most complete demonstration of a given task. IRIS breaks down
tasks into sequences of intermediate subgoals. Then it performs the actions
required to accomplish each subgoal. In this way, the agent learns from the
best parts of each demonstration and combines them to accomplish the task.
**How it works:** IRIS includes a subgoal selection model that predicts
intermediate points on the way to accomplishing an assigned task. These
subgoals are defined automatically by the algorithm, and may not correspond to
parts of a task as humans would describe them. A controller network tries to
replicate the optimal sequence of actions leading to a given subgoal.
* The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward.
* The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations.
* Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on.
**Results:** In the Robosuite's lifting and pick-and-place tasks, previous
state-of-the-art BRL approaches couldn't pick up objects reliably, nor place
them elsewhere at all. IRIS learned to pick up objects with over 80 percent
success and placed them with 30 percent success.
**Why it matters:** Automatically identifying subgoals has been a holy grail
in reinforcement learning, with active research in hierarchical RL and other
areas. The method used in this paper applies to relatively simple tasks where
things happen in a predictable sequence (such as picking and then placing),
but might be a small step in an important direction.
**We’re thinking:** Batch reinforcement learning is useful when a model must
be interpretable or safe — after all, a robotic surgeon shouldn’t experiment
on living patients — but it hasn’t been terribly effective. IRIS could make it
a viable option.
Dec 11, 2019
Issue 34
**Seeing the World Blindfolded**
In reinforcement learning, if researchers want an agent to have an internal
representation of its environment, they’ll build and train a world model that
it can refer to. New research shows that world models can emerge from standard
training, rather than needing to be built separately.
**What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and
David Ha enabled an agent to build a world model by blindfolding it as it
learned to accomplish tasks. They call their approach[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational
dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ).
**Key insight:** Blocking an agent's observations of the world at random
moments forces it to generate its own internal representation to fill in the
gaps. The agent learns this representation without being instructed to predict
how the environment will change in response to its actions.
**How it works:** At every timestep, the agent acts on either its observation
(framed in red in the video above) or its prediction of what it wasn’t able to
observe (imagery not framed in red). The agent contains a controller that
decides on the most rewarding action. To compute the potential reward of a
given action, the agent includes an additional deep net trained using the RL
algorithm REINFORCE.
* Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation.
* If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one.
* This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model.
**Results:** Observational dropout solved the task known as[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW),
in which the model must balance a pole upright on a rolling cart, even when
its view of the world was blocked 90 percent of the time. In a more complex[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car
Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)
task, in which a model must navigate a car around a track as fast as possible,
the model performed almost equally well whether it was allowed to see its
surroundings or blindfolded up to 60 percent of the time.
**Why it matters:** Modeling reality is often part art and part science.
World models generated by observational dropout aren’t perfect
representations, but they’re sufficient for some tasks. This work could lead
to simple-but-effective world models of complex environments that are
impractical to model completely.
**We’re thinking:** Technology being imperfect, observational dropout is a
fact of life, not just a research technique. A self-driving car or auto-
piloted airplane reliant on sensors that drop data points could create a
catastrophe. This technique could make high-stakes RL models more robust.
Dec 4, 2019
Issue 33
**Is AI Making Mastery Obsolete?**
Is there any reason to continue playing games that AI has mastered? Ask the
former champions who have been toppled by machines.
**What happened:** In 2016, International Go master Lee Sedol famously lost
three out of four matches to DeepMind’s AlphaGo model. The 36-year-old
announced his retirement from competition on November 27. “Even if I become
the number one, there is an entity that cannot be defeated,” he[
](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)
South Korean's Yonhap News Agency,
**Stages of grief:** Prior to the tournament, Lee predicted that he would
defeat AlphaGo easily. But the model’s inexplicable — and indefatigable —
playing style pushed him into fits of[
](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and
disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[
](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the
South Korean public.
**Reaching acceptance:** Garry Kasparov, the former world-champion chess
player, went through his own cycle of grief after being defeated by IBM’s
DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s
engineers of cheating. He later retracted the charge, and in 2017 wrote a
book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if
humans can overcome their feelings of being threatened by AI, they can learn
from it. The book advocates an augmented intelligence in which humans and
machines work together to solve problems.
**The human element:** Although AlphaGo won in the 2016 duel, its human
opponent still managed to shine. During the fourth match, Sedol made a[
](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it
defied AlphaGo’s expectation and led to his sole victory.
**We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by
a machine built by humans under the direction of AlphaGo research lead David
Silver. Human mastery is obsolete only if you ignore people like Silver and
his team.
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# My paper: A Comprehensive Review on Deep Reinforcement Learning
The updates
2021
YouTube
Notes and info
Links:
Reading List (Video, Conference, Workshop, Paper)
###
The updates
Dear friends,
I recently wrote a survey paper on "A Comprehensive Review on Deep
Reinforcement Learning: A Survey", with some of the leading AI and DRL
researchers (including): In this work, we covered top recent DRL works,
grouped into several categories. We were lucky to have you, as the external
reviewers of this work. I hope this is useful for the research community. Any
feedback will be highly welcomed. You can find its summary here too. Imitation
learning, expert (teacher), hierarchical, hybrid imitation, high performance
parallelism,
#
2021
* [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo)
* [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2)
###
YouTube
* Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE)
* Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0)
* Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU)
* Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4)
* Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0)
* How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o)
* Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA)
* Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4)
* **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG)
###
Notes and info
* training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world
* Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive.
* using reinforcement learning to train robots that reason about how their actions will affect their environment.
* How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.
* In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video?
* One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon.
* Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI.
* DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design
###
Links:
* [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U)
###
Reading List (Video, Conference, Workshop, Paper)
* [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4)
DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For
Machine Learning
* Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)
* Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)
* Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)
Google to release DeepMind's StreetLearn for teaching machine-learning agents
to navigate cities
[https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn-
for-teaching-machine-learning-agents-to-navigate-
cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle-
to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to-
navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI)
Scalable agent alignment via reward modeling – DeepMind Safety Research –
Medium
[https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via-
reward-modeling-
bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable-
agent-alignment-via-reward-modeling-
bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR)
Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech
[https://www.extremetech.com/extreme/292409-googles-deepmind-can-support-
defeat-human-players-in-quake-iii-
arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles-
deepmind-can-support-defeat-human-players-in-quake-iii-
arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy)
[https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-)
| You searched for deep mind - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai-
moves-board-games-starcraft-
ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II -
ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges
Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-
unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google-
deepmind-ai-
unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's
DeepMind AI gets a few new tricks to learn faster[
](https://www.youtube.com/results)?
Robot arm
**There are 4 Courses in this Specialization**
**Course** 1
[ **Fundamentals of Reinforcement
Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals-
of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm)
4.8
stars
801 ratings
•
205 reviews
Reinforcement Learning is a subfield of Machine Learning, but is also a
general purpose formalism for automated decision-making and AI. This course
introduces you to statistical learning techniques where an agent explicitly
takes actions and interacts with the world. Understanding the importance and
challenges of learning agents that make decisions is of vital importance
today, with more and more companies interested in interactive agents and
intelligent decision-making.
This course introduces you to the fundamentals of Reinforcement Learning. When
you finish this course, you will: - Formalize problems as Markov Decision
Processes - Understand basic exploration methods and the
exploration/exploitation tradeoff - Understand value functions, as a general-
purpose tool for optimal decision-making - Know how to implement dynamic
programming as an efficient solution approach to an industrial control problem
This course teaches you the key concepts of Reinforcement Learning, underlying
classic and modern algorithms in RL. After completing this course, you will be
able to start using RL for real problems, where you have or can specify the
MDP. This is the first course of the Reinforcement Learning Specialization.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 2
[ **Sample-based Learning
Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample-
based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ)
4.8
stars
397 ratings
•
75 reviews
In this course, you will learn about several algorithms that can learn near
optimal policies based on trial and error interaction with the environment---
learning from the agent’s own experience. Learning from actual experience is
striking because it requires no prior knowledge of the environment’s dynamics,
yet can still attain optimal behavior. We will cover intuitively simple but
powerful Monte Carlo methods, and temporal difference learning methods
including Q-learning. We will wrap up this course investigating how we can get
the best of both worlds: algorithms that can combine model-based planning
(similar to dynamic programming) and temporal difference updates to radically
accelerate learning.
By the end of this course you will be able to: - Understand Temporal-
Difference learning and Monte Carlo as two strategies for estimating value
functions from sampled experience - Understand the importance of exploration,
when using sampled experience rather than dynamic programming sweeps within a
model - Understand the connections between Monte Carlo and Dynamic Programming
and TD. - Implement and apply the TD algorithm, for estimating value functions
- Implement and apply Expected Sarsa and Q-learning (two TD methods for
control) - Understand the difference between on-policy and off-policy control
- Understand planning with simulated experience (as opposed to classic
planning strategies) - Implement a model-based approach to RL, called Dyna,
which uses simulated experience - Conduct an empirical study to see the
improvements in sample efficiency when using Dyna
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 3
[ **Prediction and Control with Function
Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction-
control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH)
4.8
stars
252 ratings
•
40 reviews
In this course, you will learn how to solve problems with large, high-
dimensional, and potentially infinite state spaces. You will see that
estimating value functions can be cast as a supervised learning problem---
function approximation---allowing you to build agents that carefully balance
generalization and discrimination in order to maximize reward. We will begin
this journey by investigating how our policy evaluation or prediction methods
like Monte Carlo and TD can be extended to the function approximation setting.
You will learn about feature construction techniques for RL, and
representation learning via neural networks and backprop. We conclude this
course with a deep-dive into policy gradient methods; a way to learn policies
directly without learning a value function. In this course you will solve two
continuous-state control tasks and investigate the benefits of policy gradient
methods in a continuous-action environment.
Prerequisites: This course strongly builds on the fundamentals of Courses 1
and 2, and learners should have completed these before starting this course.
Learners should also be comfortable with probabilities & expectations, basic
linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing
algorithms from pseudocode. By the end of this course, you will be able to:
-Understand how to use supervised learning approaches to approximate value
functions -Understand objectives for prediction (value estimation) under
function approximation -Implement TD with function approximation (state
aggregation), on an environment with an infinite state space (continuous state
space) -Understand fixed basis and neural network approaches to feature
construction -Implement TD with neural network function approximation in a
continuous state environment -Understand new difficulties in exploration when
moving to function approximation -Contrast discounted problem formulations for
control versus an average reward problem formulation -Implement expected Sarsa
and Q-learning with function approximation on a continuous state control task
-Understand objectives for directly estimating policies (policy gradient
objectives) -Implement a policy gradient method (called Actor-Critic) on a
discrete state environment
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 4
[ **A Complete Reinforcement Learning System
(Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete-
reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm)
4.6
stars
177 ratings
•
33 reviews
In this final course, you will put together your knowledge from Courses 1, 2
and 3 to implement a complete RL solution to a problem. This capstone will let
you see how each component---problem formulation, algorithm selection,
parameter selection and representation design---fits together into a complete
solution, and how to make appropriate choices when deploying RL in the real
world. This project will require you to implement both the environment to
stimulate your problem, and a control agent with Neural Network function
approximation. In addition, you will conduct a scientific study of your
learning system to develop your ability to assess the robustness of RL agents.
To use RL in the real world, it is critical to (a) appropriately formalize the
problem as an MDP, (b) select appropriate algorithms, (c ) identify what
choices in your implementation will have large impacts on performance and (d)
validate the expected behaviour of your algorithms. This capstone is valuable
for anyone who is planning on using RL to solve real problems.
To be successful in this course, you will need to have completed Courses 1, 2,
and 3 of this Specialization or the equivalent. By the end of this course, you
will be able to: Complete an RL solution to a problem, starting from problem
formulation, appropriate algorithm selection and implementation and empirical
study into the effectiveness of the solution.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Using pre trained model to train deeper and lager model**
Imitation Learning
Safety Gym, a suite of environments and tools for measuring progress towards
reinforcement learning agents that respect safety constraints while training.
It also provides a standardized method of comparing algorithms and how well
they avoid costly mistakes while learning. If deep reinforcement learning is
applied to the real world, whether in robotics or internet-based tasks, it
will be important to have algorithms that are safe even while learning—like a
self-driving car that can learn to avoid accidents without actually having to
experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/
Datascience posts:[
](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)
[OpenAI Safety Gym: A Safe Place For AIs To Learn
💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo)
**DeepMind proposes novel way to train ‘safe’ reinforcement learning AI**
[https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe-
reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind-
proposes-novel-way-to-train-safe-reinforcement-learning-
ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ-
HA8tJ)
**The Batch** Issue 35
**Different Skills From Different Demos**
Reinforcement learning trains models by trial and error. In batch
reinforcement learning (BRL), models learn by observing many demonstrations by
a variety of actors. For instance, a robot might learn how to fix ingrown
toenails by watching hundreds of surgeons perform the procedure. But what if
one doctor is handier with a scalpel while another excels at suturing? A new
method lets models absorb the best skills from each.
**What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the
University of Toronto devised a BRL technique that enables models to learn
different portions of a task from different examples. This way, the model can
gain useful information from inconsistent examples.[
](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit
Reinforcement without Interaction at
Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)
(IRIS) achieved state-of-the-art BRL performance in three tasks performed in a
virtual environment.
**Key insight:** Learning from demonstrations is a double-edged sword. An
agent gets to see how to complete a task, but the scope of its action is
limited to the most complete demonstration of a given task. IRIS breaks down
tasks into sequences of intermediate subgoals. Then it performs the actions
required to accomplish each subgoal. In this way, the agent learns from the
best parts of each demonstration and combines them to accomplish the task.
**How it works:** IRIS includes a subgoal selection model that predicts
intermediate points on the way to accomplishing an assigned task. These
subgoals are defined automatically by the algorithm, and may not correspond to
parts of a task as humans would describe them. A controller network tries to
replicate the optimal sequence of actions leading to a given subgoal.
* The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward.
* The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations.
* Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on.
**Results:** In the Robosuite's lifting and pick-and-place tasks, previous
state-of-the-art BRL approaches couldn't pick up objects reliably, nor place
them elsewhere at all. IRIS learned to pick up objects with over 80 percent
success and placed them with 30 percent success.
**Why it matters:** Automatically identifying subgoals has been a holy grail
in reinforcement learning, with active research in hierarchical RL and other
areas. The method used in this paper applies to relatively simple tasks where
things happen in a predictable sequence (such as picking and then placing),
but might be a small step in an important direction.
**We’re thinking:** Batch reinforcement learning is useful when a model must
be interpretable or safe — after all, a robotic surgeon shouldn’t experiment
on living patients — but it hasn’t been terribly effective. IRIS could make it
a viable option.
Dec 11, 2019
Issue 34
**Seeing the World Blindfolded**
In reinforcement learning, if researchers want an agent to have an internal
representation of its environment, they’ll build and train a world model that
it can refer to. New research shows that world models can emerge from standard
training, rather than needing to be built separately.
**What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and
David Ha enabled an agent to build a world model by blindfolding it as it
learned to accomplish tasks. They call their approach[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational
dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ).
**Key insight:** Blocking an agent's observations of the world at random
moments forces it to generate its own internal representation to fill in the
gaps. The agent learns this representation without being instructed to predict
how the environment will change in response to its actions.
**How it works:** At every timestep, the agent acts on either its observation
(framed in red in the video above) or its prediction of what it wasn’t able to
observe (imagery not framed in red). The agent contains a controller that
decides on the most rewarding action. To compute the potential reward of a
given action, the agent includes an additional deep net trained using the RL
algorithm REINFORCE.
* Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation.
* If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one.
* This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model.
**Results:** Observational dropout solved the task known as[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW),
in which the model must balance a pole upright on a rolling cart, even when
its view of the world was blocked 90 percent of the time. In a more complex[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car
Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)
task, in which a model must navigate a car around a track as fast as possible,
the model performed almost equally well whether it was allowed to see its
surroundings or blindfolded up to 60 percent of the time.
**Why it matters:** Modeling reality is often part art and part science.
World models generated by observational dropout aren’t perfect
representations, but they’re sufficient for some tasks. This work could lead
to simple-but-effective world models of complex environments that are
impractical to model completely.
**We’re thinking:** Technology being imperfect, observational dropout is a
fact of life, not just a research technique. A self-driving car or auto-
piloted airplane reliant on sensors that drop data points could create a
catastrophe. This technique could make high-stakes RL models more robust.
Dec 4, 2019
Issue 33
**Is AI Making Mastery Obsolete?**
Is there any reason to continue playing games that AI has mastered? Ask the
former champions who have been toppled by machines.
**What happened:** In 2016, International Go master Lee Sedol famously lost
three out of four matches to DeepMind’s AlphaGo model. The 36-year-old
announced his retirement from competition on November 27. “Even if I become
the number one, there is an entity that cannot be defeated,” he[
](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)
South Korean's Yonhap News Agency,
**Stages of grief:** Prior to the tournament, Lee predicted that he would
defeat AlphaGo easily. But the model’s inexplicable — and indefatigable —
playing style pushed him into fits of[
](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and
disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[
](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the
South Korean public.
**Reaching acceptance:** Garry Kasparov, the former world-champion chess
player, went through his own cycle of grief after being defeated by IBM’s
DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s
engineers of cheating. He later retracted the charge, and in 2017 wrote a
book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if
humans can overcome their feelings of being threatened by AI, they can learn
from it. The book advocates an augmented intelligence in which humans and
machines work together to solve problems.
**The human element:** Although AlphaGo won in the 2016 duel, its human
opponent still managed to shine. During the fourth match, Sedol made a[
](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it
defied AlphaGo’s expectation and led to his sole victory.
**We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by
a machine built by humans under the direction of AlphaGo research lead David
Silver. Human mastery is obsolete only if you ignore people like Silver and
his team.
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# My paper: A Comprehensive Review on Deep Reinforcement Learning
The updates
2021
YouTube
Notes and info
Links:
Reading List (Video, Conference, Workshop, Paper)
###
The updates
Dear friends,
I recently wrote a survey paper on "A Comprehensive Review on Deep
Reinforcement Learning: A Survey", with some of the leading AI and DRL
researchers (including): In this work, we covered top recent DRL works,
grouped into several categories. We were lucky to have you, as the external
reviewers of this work. I hope this is useful for the research community. Any
feedback will be highly welcomed. You can find its summary here too. Imitation
learning, expert (teacher), hierarchical, hybrid imitation, high performance
parallelism,
#
2021
* [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo)
* [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2)
###
YouTube
* Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE)
* Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0)
* Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU)
* Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4)
* Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0)
* How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o)
* Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA)
* Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4)
* **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG)
###
Notes and info
* training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world
* Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive.
* using reinforcement learning to train robots that reason about how their actions will affect their environment.
* How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.
* In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video?
* One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon.
* Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI.
* DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design
###
Links:
* [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U)
###
Reading List (Video, Conference, Workshop, Paper)
* [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4)
DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For
Machine Learning
* Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)
* Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)
* Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)
Google to release DeepMind's StreetLearn for teaching machine-learning agents
to navigate cities
[https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn-
for-teaching-machine-learning-agents-to-navigate-
cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle-
to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to-
navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI)
Scalable agent alignment via reward modeling – DeepMind Safety Research –
Medium
[https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via-
reward-modeling-
bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable-
agent-alignment-via-reward-modeling-
bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR)
Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech
[https://www.extremetech.com/extreme/292409-googles-deepmind-can-support-
defeat-human-players-in-quake-iii-
arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles-
deepmind-can-support-defeat-human-players-in-quake-iii-
arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy)
[https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-)
| You searched for deep mind - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai-
moves-board-games-starcraft-
ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II -
ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges
Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-
unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google-
deepmind-ai-
unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's
DeepMind AI gets a few new tricks to learn faster[
](https://www.youtube.com/results)?
Robot arm
**There are 4 Courses in this Specialization**
**Course** 1
[ **Fundamentals of Reinforcement
Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals-
of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm)
4.8
stars
801 ratings
•
205 reviews
Reinforcement Learning is a subfield of Machine Learning, but is also a
general purpose formalism for automated decision-making and AI. This course
introduces you to statistical learning techniques where an agent explicitly
takes actions and interacts with the world. Understanding the importance and
challenges of learning agents that make decisions is of vital importance
today, with more and more companies interested in interactive agents and
intelligent decision-making.
This course introduces you to the fundamentals of Reinforcement Learning. When
you finish this course, you will: - Formalize problems as Markov Decision
Processes - Understand basic exploration methods and the
exploration/exploitation tradeoff - Understand value functions, as a general-
purpose tool for optimal decision-making - Know how to implement dynamic
programming as an efficient solution approach to an industrial control problem
This course teaches you the key concepts of Reinforcement Learning, underlying
classic and modern algorithms in RL. After completing this course, you will be
able to start using RL for real problems, where you have or can specify the
MDP. This is the first course of the Reinforcement Learning Specialization.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 2
[ **Sample-based Learning
Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample-
based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ)
4.8
stars
397 ratings
•
75 reviews
In this course, you will learn about several algorithms that can learn near
optimal policies based on trial and error interaction with the environment---
learning from the agent’s own experience. Learning from actual experience is
striking because it requires no prior knowledge of the environment’s dynamics,
yet can still attain optimal behavior. We will cover intuitively simple but
powerful Monte Carlo methods, and temporal difference learning methods
including Q-learning. We will wrap up this course investigating how we can get
the best of both worlds: algorithms that can combine model-based planning
(similar to dynamic programming) and temporal difference updates to radically
accelerate learning.
By the end of this course you will be able to: - Understand Temporal-
Difference learning and Monte Carlo as two strategies for estimating value
functions from sampled experience - Understand the importance of exploration,
when using sampled experience rather than dynamic programming sweeps within a
model - Understand the connections between Monte Carlo and Dynamic Programming
and TD. - Implement and apply the TD algorithm, for estimating value functions
- Implement and apply Expected Sarsa and Q-learning (two TD methods for
control) - Understand the difference between on-policy and off-policy control
- Understand planning with simulated experience (as opposed to classic
planning strategies) - Implement a model-based approach to RL, called Dyna,
which uses simulated experience - Conduct an empirical study to see the
improvements in sample efficiency when using Dyna
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 3
[ **Prediction and Control with Function
Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction-
control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH)
4.8
stars
252 ratings
•
40 reviews
In this course, you will learn how to solve problems with large, high-
dimensional, and potentially infinite state spaces. You will see that
estimating value functions can be cast as a supervised learning problem---
function approximation---allowing you to build agents that carefully balance
generalization and discrimination in order to maximize reward. We will begin
this journey by investigating how our policy evaluation or prediction methods
like Monte Carlo and TD can be extended to the function approximation setting.
You will learn about feature construction techniques for RL, and
representation learning via neural networks and backprop. We conclude this
course with a deep-dive into policy gradient methods; a way to learn policies
directly without learning a value function. In this course you will solve two
continuous-state control tasks and investigate the benefits of policy gradient
methods in a continuous-action environment.
Prerequisites: This course strongly builds on the fundamentals of Courses 1
and 2, and learners should have completed these before starting this course.
Learners should also be comfortable with probabilities & expectations, basic
linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing
algorithms from pseudocode. By the end of this course, you will be able to:
-Understand how to use supervised learning approaches to approximate value
functions -Understand objectives for prediction (value estimation) under
function approximation -Implement TD with function approximation (state
aggregation), on an environment with an infinite state space (continuous state
space) -Understand fixed basis and neural network approaches to feature
construction -Implement TD with neural network function approximation in a
continuous state environment -Understand new difficulties in exploration when
moving to function approximation -Contrast discounted problem formulations for
control versus an average reward problem formulation -Implement expected Sarsa
and Q-learning with function approximation on a continuous state control task
-Understand objectives for directly estimating policies (policy gradient
objectives) -Implement a policy gradient method (called Actor-Critic) on a
discrete state environment
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 4
[ **A Complete Reinforcement Learning System
(Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete-
reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm)
4.6
stars
177 ratings
•
33 reviews
In this final course, you will put together your knowledge from Courses 1, 2
and 3 to implement a complete RL solution to a problem. This capstone will let
you see how each component---problem formulation, algorithm selection,
parameter selection and representation design---fits together into a complete
solution, and how to make appropriate choices when deploying RL in the real
world. This project will require you to implement both the environment to
stimulate your problem, and a control agent with Neural Network function
approximation. In addition, you will conduct a scientific study of your
learning system to develop your ability to assess the robustness of RL agents.
To use RL in the real world, it is critical to (a) appropriately formalize the
problem as an MDP, (b) select appropriate algorithms, (c ) identify what
choices in your implementation will have large impacts on performance and (d)
validate the expected behaviour of your algorithms. This capstone is valuable
for anyone who is planning on using RL to solve real problems.
To be successful in this course, you will need to have completed Courses 1, 2,
and 3 of this Specialization or the equivalent. By the end of this course, you
will be able to: Complete an RL solution to a problem, starting from problem
formulation, appropriate algorithm selection and implementation and empirical
study into the effectiveness of the solution.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Using pre trained model to train deeper and lager model**
Imitation Learning
Safety Gym, a suite of environments and tools for measuring progress towards
reinforcement learning agents that respect safety constraints while training.
It also provides a standardized method of comparing algorithms and how well
they avoid costly mistakes while learning. If deep reinforcement learning is
applied to the real world, whether in robotics or internet-based tasks, it
will be important to have algorithms that are safe even while learning—like a
self-driving car that can learn to avoid accidents without actually having to
experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/
Datascience posts:[
](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)
[OpenAI Safety Gym: A Safe Place For AIs To Learn
💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo)
**DeepMind proposes novel way to train ‘safe’ reinforcement learning AI**
[https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe-
reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind-
proposes-novel-way-to-train-safe-reinforcement-learning-
ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ-
HA8tJ)
**The Batch** Issue 35
**Different Skills From Different Demos**
Reinforcement learning trains models by trial and error. In batch
reinforcement learning (BRL), models learn by observing many demonstrations by
a variety of actors. For instance, a robot might learn how to fix ingrown
toenails by watching hundreds of surgeons perform the procedure. But what if
one doctor is handier with a scalpel while another excels at suturing? A new
method lets models absorb the best skills from each.
**What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the
University of Toronto devised a BRL technique that enables models to learn
different portions of a task from different examples. This way, the model can
gain useful information from inconsistent examples.[
](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit
Reinforcement without Interaction at
Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)
(IRIS) achieved state-of-the-art BRL performance in three tasks performed in a
virtual environment.
**Key insight:** Learning from demonstrations is a double-edged sword. An
agent gets to see how to complete a task, but the scope of its action is
limited to the most complete demonstration of a given task. IRIS breaks down
tasks into sequences of intermediate subgoals. Then it performs the actions
required to accomplish each subgoal. In this way, the agent learns from the
best parts of each demonstration and combines them to accomplish the task.
**How it works:** IRIS includes a subgoal selection model that predicts
intermediate points on the way to accomplishing an assigned task. These
subgoals are defined automatically by the algorithm, and may not correspond to
parts of a task as humans would describe them. A controller network tries to
replicate the optimal sequence of actions leading to a given subgoal.
* The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward.
* The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations.
* Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on.
**Results:** In the Robosuite's lifting and pick-and-place tasks, previous
state-of-the-art BRL approaches couldn't pick up objects reliably, nor place
them elsewhere at all. IRIS learned to pick up objects with over 80 percent
success and placed them with 30 percent success.
**Why it matters:** Automatically identifying subgoals has been a holy grail
in reinforcement learning, with active research in hierarchical RL and other
areas. The method used in this paper applies to relatively simple tasks where
things happen in a predictable sequence (such as picking and then placing),
but might be a small step in an important direction.
**We’re thinking:** Batch reinforcement learning is useful when a model must
be interpretable or safe — after all, a robotic surgeon shouldn’t experiment
on living patients — but it hasn’t been terribly effective. IRIS could make it
a viable option.
Dec 11, 2019
Issue 34
**Seeing the World Blindfolded**
In reinforcement learning, if researchers want an agent to have an internal
representation of its environment, they’ll build and train a world model that
it can refer to. New research shows that world models can emerge from standard
training, rather than needing to be built separately.
**What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and
David Ha enabled an agent to build a world model by blindfolding it as it
learned to accomplish tasks. They call their approach[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational
dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ).
**Key insight:** Blocking an agent's observations of the world at random
moments forces it to generate its own internal representation to fill in the
gaps. The agent learns this representation without being instructed to predict
how the environment will change in response to its actions.
**How it works:** At every timestep, the agent acts on either its observation
(framed in red in the video above) or its prediction of what it wasn’t able to
observe (imagery not framed in red). The agent contains a controller that
decides on the most rewarding action. To compute the potential reward of a
given action, the agent includes an additional deep net trained using the RL
algorithm REINFORCE.
* Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation.
* If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one.
* This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model.
**Results:** Observational dropout solved the task known as[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW),
in which the model must balance a pole upright on a rolling cart, even when
its view of the world was blocked 90 percent of the time. In a more complex[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car
Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)
task, in which a model must navigate a car around a track as fast as possible,
the model performed almost equally well whether it was allowed to see its
surroundings or blindfolded up to 60 percent of the time.
**Why it matters:** Modeling reality is often part art and part science.
World models generated by observational dropout aren’t perfect
representations, but they’re sufficient for some tasks. This work could lead
to simple-but-effective world models of complex environments that are
impractical to model completely.
**We’re thinking:** Technology being imperfect, observational dropout is a
fact of life, not just a research technique. A self-driving car or auto-
piloted airplane reliant on sensors that drop data points could create a
catastrophe. This technique could make high-stakes RL models more robust.
Dec 4, 2019
Issue 33
**Is AI Making Mastery Obsolete?**
Is there any reason to continue playing games that AI has mastered? Ask the
former champions who have been toppled by machines.
**What happened:** In 2016, International Go master Lee Sedol famously lost
three out of four matches to DeepMind’s AlphaGo model. The 36-year-old
announced his retirement from competition on November 27. “Even if I become
the number one, there is an entity that cannot be defeated,” he[
](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)
South Korean's Yonhap News Agency,
**Stages of grief:** Prior to the tournament, Lee predicted that he would
defeat AlphaGo easily. But the model’s inexplicable — and indefatigable —
playing style pushed him into fits of[
](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and
disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[
](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the
South Korean public.
**Reaching acceptance:** Garry Kasparov, the former world-champion chess
player, went through his own cycle of grief after being defeated by IBM’s
DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s
engineers of cheating. He later retracted the charge, and in 2017 wrote a
book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if
humans can overcome their feelings of being threatened by AI, they can learn
from it. The book advocates an augmented intelligence in which humans and
machines work together to solve problems.
**The human element:** Although AlphaGo won in the 2016 duel, its human
opponent still managed to shine. During the fourth match, Sedol made a[
](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it
defied AlphaGo’s expectation and led to his sole victory.
**We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by
a machine built by humans under the direction of AlphaGo research lead David
Silver. Human mastery is obsolete only if you ignore people like Silver and
his team.
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# My paper: A Comprehensive Review on Deep Reinforcement Learning
The updates
2021
YouTube
Notes and info
Links:
Reading List (Video, Conference, Workshop, Paper)
###
The updates
Dear friends,
I recently wrote a survey paper on "A Comprehensive Review on Deep
Reinforcement Learning: A Survey", with some of the leading AI and DRL
researchers (including): In this work, we covered top recent DRL works,
grouped into several categories. We were lucky to have you, as the external
reviewers of this work. I hope this is useful for the research community. Any
feedback will be highly welcomed. You can find its summary here too. Imitation
learning, expert (teacher), hierarchical, hybrid imitation, high performance
parallelism,
#
2021
* [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo)
* [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2)
###
YouTube
* Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE)
* Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0)
* Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU)
* Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4)
* Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0)
* How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o)
* Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA)
* Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4)
* **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG)
###
Notes and info
* training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world
* Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive.
* using reinforcement learning to train robots that reason about how their actions will affect their environment.
* How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.
* In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video?
* One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon.
* Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI.
* DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design
###
Links:
* [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U)
###
Reading List (Video, Conference, Workshop, Paper)
* [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4)
DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For
Machine Learning
* Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)
* Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)
* Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)
Google to release DeepMind's StreetLearn for teaching machine-learning agents
to navigate cities
[https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn-
for-teaching-machine-learning-agents-to-navigate-
cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle-
to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to-
navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI)
Scalable agent alignment via reward modeling – DeepMind Safety Research –
Medium
[https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via-
reward-modeling-
bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable-
agent-alignment-via-reward-modeling-
bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR)
Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech
[https://www.extremetech.com/extreme/292409-googles-deepmind-can-support-
defeat-human-players-in-quake-iii-
arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles-
deepmind-can-support-defeat-human-players-in-quake-iii-
arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy)
[https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-)
| You searched for deep mind - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai-
moves-board-games-starcraft-
ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind-
ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm-
oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II -
ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind-
ai-challenges-pro-starcraft-ii-players-wins-almost-every-
match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges
Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[
](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-
unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google-
deepmind-ai-
unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle-
deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's
DeepMind AI gets a few new tricks to learn faster[
](https://www.youtube.com/results)?
Robot arm
**There are 4 Courses in this Specialization**
**Course** 1
[ **Fundamentals of Reinforcement
Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals-
of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm)
4.8
stars
801 ratings
•
205 reviews
Reinforcement Learning is a subfield of Machine Learning, but is also a
general purpose formalism for automated decision-making and AI. This course
introduces you to statistical learning techniques where an agent explicitly
takes actions and interacts with the world. Understanding the importance and
challenges of learning agents that make decisions is of vital importance
today, with more and more companies interested in interactive agents and
intelligent decision-making.
This course introduces you to the fundamentals of Reinforcement Learning. When
you finish this course, you will: - Formalize problems as Markov Decision
Processes - Understand basic exploration methods and the
exploration/exploitation tradeoff - Understand value functions, as a general-
purpose tool for optimal decision-making - Know how to implement dynamic
programming as an efficient solution approach to an industrial control problem
This course teaches you the key concepts of Reinforcement Learning, underlying
classic and modern algorithms in RL. After completing this course, you will be
able to start using RL for real problems, where you have or can specify the
MDP. This is the first course of the Reinforcement Learning Specialization.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 2
[ **Sample-based Learning
Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample-
based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ)
4.8
stars
397 ratings
•
75 reviews
In this course, you will learn about several algorithms that can learn near
optimal policies based on trial and error interaction with the environment---
learning from the agent’s own experience. Learning from actual experience is
striking because it requires no prior knowledge of the environment’s dynamics,
yet can still attain optimal behavior. We will cover intuitively simple but
powerful Monte Carlo methods, and temporal difference learning methods
including Q-learning. We will wrap up this course investigating how we can get
the best of both worlds: algorithms that can combine model-based planning
(similar to dynamic programming) and temporal difference updates to radically
accelerate learning.
By the end of this course you will be able to: - Understand Temporal-
Difference learning and Monte Carlo as two strategies for estimating value
functions from sampled experience - Understand the importance of exploration,
when using sampled experience rather than dynamic programming sweeps within a
model - Understand the connections between Monte Carlo and Dynamic Programming
and TD. - Implement and apply the TD algorithm, for estimating value functions
- Implement and apply Expected Sarsa and Q-learning (two TD methods for
control) - Understand the difference between on-policy and off-policy control
- Understand planning with simulated experience (as opposed to classic
planning strategies) - Implement a model-based approach to RL, called Dyna,
which uses simulated experience - Conduct an empirical study to see the
improvements in sample efficiency when using Dyna
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 3
[ **Prediction and Control with Function
Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction-
control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH)
4.8
stars
252 ratings
•
40 reviews
In this course, you will learn how to solve problems with large, high-
dimensional, and potentially infinite state spaces. You will see that
estimating value functions can be cast as a supervised learning problem---
function approximation---allowing you to build agents that carefully balance
generalization and discrimination in order to maximize reward. We will begin
this journey by investigating how our policy evaluation or prediction methods
like Monte Carlo and TD can be extended to the function approximation setting.
You will learn about feature construction techniques for RL, and
representation learning via neural networks and backprop. We conclude this
course with a deep-dive into policy gradient methods; a way to learn policies
directly without learning a value function. In this course you will solve two
continuous-state control tasks and investigate the benefits of policy gradient
methods in a continuous-action environment.
Prerequisites: This course strongly builds on the fundamentals of Courses 1
and 2, and learners should have completed these before starting this course.
Learners should also be comfortable with probabilities & expectations, basic
linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing
algorithms from pseudocode. By the end of this course, you will be able to:
-Understand how to use supervised learning approaches to approximate value
functions -Understand objectives for prediction (value estimation) under
function approximation -Implement TD with function approximation (state
aggregation), on an environment with an infinite state space (continuous state
space) -Understand fixed basis and neural network approaches to feature
construction -Implement TD with neural network function approximation in a
continuous state environment -Understand new difficulties in exploration when
moving to function approximation -Contrast discounted problem formulations for
control versus an average reward problem formulation -Implement expected Sarsa
and Q-learning with function approximation on a continuous state control task
-Understand objectives for directly estimating policies (policy gradient
objectives) -Implement a policy gradient method (called Actor-Critic) on a
discrete state environment
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Course** 4
[ **A Complete Reinforcement Learning System
(Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete-
reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm)
4.6
stars
177 ratings
•
33 reviews
In this final course, you will put together your knowledge from Courses 1, 2
and 3 to implement a complete RL solution to a problem. This capstone will let
you see how each component---problem formulation, algorithm selection,
parameter selection and representation design---fits together into a complete
solution, and how to make appropriate choices when deploying RL in the real
world. This project will require you to implement both the environment to
stimulate your problem, and a control agent with Neural Network function
approximation. In addition, you will conduct a scientific study of your
learning system to develop your ability to assess the robustness of RL agents.
To use RL in the real world, it is critical to (a) appropriately formalize the
problem as an MDP, (b) select appropriate algorithms, (c ) identify what
choices in your implementation will have large impacts on performance and (d)
validate the expected behaviour of your algorithms. This capstone is valuable
for anyone who is planning on using RL to solve real problems.
To be successful in this course, you will need to have completed Courses 1, 2,
and 3 of this Specialization or the equivalent. By the end of this course, you
will be able to: Complete an RL solution to a problem, starting from problem
formulation, appropriate algorithm selection and implementation and empirical
study into the effectiveness of the solution.
[SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW
ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-
learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq)
**Using pre trained model to train deeper and lager model**
Imitation Learning
Safety Gym, a suite of environments and tools for measuring progress towards
reinforcement learning agents that respect safety constraints while training.
It also provides a standardized method of comparing algorithms and how well
they avoid costly mistakes while learning. If deep reinforcement learning is
applied to the real world, whether in robotics or internet-based tasks, it
will be important to have algorithms that are safe even while learning—like a
self-driving car that can learn to avoid accidents without actually having to
experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/
Datascience posts:[
](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)
[OpenAI Safety Gym: A Safe Place For AIs To Learn
💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo)
**DeepMind proposes novel way to train ‘safe’ reinforcement learning AI**
[https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe-
reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind-
proposes-novel-way-to-train-safe-reinforcement-learning-
ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP-
cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ-
HA8tJ)
**The Batch** Issue 35
**Different Skills From Different Demos**
Reinforcement learning trains models by trial and error. In batch
reinforcement learning (BRL), models learn by observing many demonstrations by
a variety of actors. For instance, a robot might learn how to fix ingrown
toenails by watching hundreds of surgeons perform the procedure. But what if
one doctor is handier with a scalpel while another excels at suturing? A new
method lets models absorb the best skills from each.
**What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the
University of Toronto devised a BRL technique that enables models to learn
different portions of a task from different examples. This way, the model can
gain useful information from inconsistent examples.[
](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit
Reinforcement without Interaction at
Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV-
QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)
(IRIS) achieved state-of-the-art BRL performance in three tasks performed in a
virtual environment.
**Key insight:** Learning from demonstrations is a double-edged sword. An
agent gets to see how to complete a task, but the scope of its action is
limited to the most complete demonstration of a given task. IRIS breaks down
tasks into sequences of intermediate subgoals. Then it performs the actions
required to accomplish each subgoal. In this way, the agent learns from the
best parts of each demonstration and combines them to accomplish the task.
**How it works:** IRIS includes a subgoal selection model that predicts
intermediate points on the way to accomplishing an assigned task. These
subgoals are defined automatically by the algorithm, and may not correspond to
parts of a task as humans would describe them. A controller network tries to
replicate the optimal sequence of actions leading to a given subgoal.
* The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward.
* The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations.
* Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on.
**Results:** In the Robosuite's lifting and pick-and-place tasks, previous
state-of-the-art BRL approaches couldn't pick up objects reliably, nor place
them elsewhere at all. IRIS learned to pick up objects with over 80 percent
success and placed them with 30 percent success.
**Why it matters:** Automatically identifying subgoals has been a holy grail
in reinforcement learning, with active research in hierarchical RL and other
areas. The method used in this paper applies to relatively simple tasks where
things happen in a predictable sequence (such as picking and then placing),
but might be a small step in an important direction.
**We’re thinking:** Batch reinforcement learning is useful when a model must
be interpretable or safe — after all, a robotic surgeon shouldn’t experiment
on living patients — but it hasn’t been terribly effective. IRIS could make it
a viable option.
Dec 11, 2019
Issue 34
**Seeing the World Blindfolded**
In reinforcement learning, if researchers want an agent to have an internal
representation of its environment, they’ll build and train a world model that
it can refer to. New research shows that world models can emerge from standard
training, rather than needing to be built separately.
**What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and
David Ha enabled an agent to build a world model by blindfolding it as it
learned to accomplish tasks. They call their approach[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational
dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ).
**Key insight:** Blocking an agent's observations of the world at random
moments forces it to generate its own internal representation to fill in the
gaps. The agent learns this representation without being instructed to predict
how the environment will change in response to its actions.
**How it works:** At every timestep, the agent acts on either its observation
(framed in red in the video above) or its prediction of what it wasn’t able to
observe (imagery not framed in red). The agent contains a controller that
decides on the most rewarding action. To compute the potential reward of a
given action, the agent includes an additional deep net trained using the RL
algorithm REINFORCE.
* Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation.
* If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one.
* This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model.
**Results:** Observational dropout solved the task known as[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW),
in which the model must balance a pole upright on a rolling cart, even when
its view of the world was blocked 90 percent of the time. In a more complex[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car
Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)
task, in which a model must navigate a car around a track as fast as possible,
the model performed almost equally well whether it was allowed to see its
surroundings or blindfolded up to 60 percent of the time.
**Why it matters:** Modeling reality is often part art and part science.
World models generated by observational dropout aren’t perfect
representations, but they’re sufficient for some tasks. This work could lead
to simple-but-effective world models of complex environments that are
impractical to model completely.
**We’re thinking:** Technology being imperfect, observational dropout is a
fact of life, not just a research technique. A self-driving car or auto-
piloted airplane reliant on sensors that drop data points could create a
catastrophe. This technique could make high-stakes RL models more robust.
Dec 4, 2019
Issue 33
**Is AI Making Mastery Obsolete?**
Is there any reason to continue playing games that AI has mastered? Ask the
former champions who have been toppled by machines.
**What happened:** In 2016, International Go master Lee Sedol famously lost
three out of four matches to DeepMind’s AlphaGo model. The 36-year-old
announced his retirement from competition on November 27. “Even if I become
the number one, there is an entity that cannot be defeated,” he[
](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)
South Korean's Yonhap News Agency,
**Stages of grief:** Prior to the tournament, Lee predicted that he would
defeat AlphaGo easily. But the model’s inexplicable — and indefatigable —
playing style pushed him into fits of[
](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and
disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago-
deepmind-ai-documentary-go-lee-sedol-film-
review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[
](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo-
board-game-champion-lee-sedol-apologizes-for-losing-to-googles-
ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the
South Korean public.
**Reaching acceptance:** Garry Kasparov, the former world-champion chess
player, went through his own cycle of grief after being defeated by IBM’s
DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s
engineers of cheating. He later retracted the charge, and in 2017 wrote a
book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry-
kasparov-says-ai-can-make-us-more-human-pcmag-interview-
march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if
humans can overcome their feelings of being threatened by AI, they can learn
from it. The book advocates an augmented intelligence in which humans and
machines work together to solve problems.
**The human element:** Although AlphaGo won in the 2016 duel, its human
opponent still managed to shine. During the fourth match, Sedol made a[
](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo-
moves-alphago-lee-sedol-redefined-
future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it
defied AlphaGo’s expectation and led to his sole victory.
**We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by
a machine built by humans under the direction of AlphaGo research lead David
Silver. Human mastery is obsolete only if you ignore people like Silver and
his team.
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# Camera_Calibration
Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended
reality/mixed reality) 3D Image Processing with Deep Learning
introduction
Source code
Reference
#
Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended
reality/mixed reality) 3D Image Processing with Deep Learning
##
introduction
Geometric camera calibration, also referred to as camera re-sectioning,
estimates the parameters of a lens and image sensor of an image or video
camera. These parameters can be used to correct for lens distortion, measure
the size of an object in world units, or determine the location of the camera
in a scene. These tasks are used in applications such as machine vision to
detect and measure objects. They are also used in robotics, navigation
systems, and 3-D scene reconstruction. Without any knowledge of the
calibration of the cameras, it is impossible to do better than projective
reconstruction (MathWorks).
Non-intrusive scene measurement tasks, such as 3D reconstruction, object
inspection, target or self-localization or scene mapping require a calibrated
camera model (Orghidan et al. 2011). Camera calibration is the process of
approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995;
Heikkila & Silven 1997) of a given photograph or video.
There are four main categories of camera calibration methods whereby a number
of algorithms have been proposed for each categories/methods, namely knowing
object based camera calibration, semi auto calibration, camera self-
calibration method, and camera calibration method based on active vision.
In computer vision methods, image information from cameras can yield geometric
information pertaining to three-dimensional objects. Non-intrusive scene
measurement tasks, such as 3D reconstruction, object inspection, target or
self-localization, or scene mapping require a calibrated camera model
(Orghidan et al. 2011). The correlation between the geographical point and
camera image pixel is necessary for camera calibration. Hence, the camera’s
parameter, which constitutes the geometric model of camera imaging, are
utilized to establish the correlation between the three-dimensional geometric
location of one point and a corresponding point in an image (Wang et al.
2010). Typically, experiments are conducted to attain the aforementioned
parameters and relevant calculation, which is a process called camera
calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014;
Navarro et al. 2014).
Image information from cameras can be used to elucidate the geometric
information of a 3D object. The process of estimating the parameters of a
pinhole camera model is called camera calibration. The more accurate the
estimated parameters, the better the compensation that can be performed for
the next stage of the application. In the data collection stage, a camera will
take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila &
Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of
pair images from both cameras via high quality images and increased range of
slope of calibration pattern. The current methods simply create images upon
the detection of calibration pattern. Nonetheless, the consensus in literature
is that accurate camera calibration necessitates pure rotation (Zhang et al.
2008) and require sharp images. Recent breakthrough methods, such as Zhang’s
(Zhengyou 2000), use fixed threshold to elucidate pixel difference between the
frames and pre-setting variables, where slope information for image frame
selection in camera calibration phase has been neglected (Audet & Okutomi
2009). Conversely, these approaches become less reliable when image frames are
blurred. These problems necessitates that the camera calibration algorithm be
enhanced (Wang et al. 2010).
OpenCV
Deep Learning
[
**https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx-
QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg-
zy1CXgeEwRHbfcCHeA=w1280**](https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx-
QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg-
zy1CXgeEwRHbfcCHeA=w1280) ****
**Engineering of Camera Calibration**
Occasionally the out-of-the-box solution does not work, and you need some
modified version of the algorithms.
The first step of camera calibration is using known pattern images, such as
chessboard. However, sometimes the image quality and pattern are not match
with standard approach of calibration process.
I use some other technique to enhance the result. In the first step, we need
to improve the corner detection, and it may be done by fallowing steps.
* The chessboard is used as a pattern of alternating black and white squares,
\- which ensures that there is no bias toward one side or the other in
measurement.
* The image must be an grayscale (single-channel) image.
\- img - Input image. It should be grayscale and float32 type.
* gradianet x and y direction together (for better detection)
\- cv.morphologyEx( src, op, kernel[, dst[, anchor[, iterations[, borderType[,
borderValue]]]]] ) -> dst # different kernel is required
* using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities.
\- cv.cornerHarris( src, blockSize, ksize, k[, dst[, borderType]] ) -> dst #
the parameters a and b and c should be modified
> img - Input image. It should be grayscale and float32 type.
> blockSize - It is the size of neighborhood considered for corner detection
> ksize - Aperture parameter of the Sobel derivative used.
> k - Harris detector free parameter in the equation.
* contours to remove some noise:
- cv.connectedComponentsWithStats( image[, labels[, stats[, centroids[, connectivity[, ltype]]]]] ) -> retval, labels, stats, centroids
* subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates
cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) -> corners
\- image Input single-channel, 8-bit or float image.
\- corners Initial coordinates of the input corners and refined coordinates
provided for output.
\- winSize Half of the side length of the search window. (5*5 will be 11)
\- zeroZone It is used sometimes to avoid possible singularities of the auto
correlation matrix.
\- criteria Criteria for termination of the iterative process of corner
refinement.
* remove duplicate corners: for example corners are in less than 5 pixels should be remove
Reference:
[https://theailearner.com/tag/cv2-cornersubpix/](https://www.google.com/url?q=https%3A%2F%2Ftheailearner.com%2Ftag%2Fcv2-cornersubpix%2F&sa=D&sntz=1&usg=AOvVaw1LDrIDpdKUACBUnVjQPB5i)
[https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fdc%2Fd0d%2Ftutorial_py_features_harris.html&sa=D&sntz=1&usg=AOvVaw28cWci42D6B_nRD0F_RXjJ)
#Camera_Calibration #Camera-resectioning
See more:[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine-
learning-specialization%2Fmachine-learning-foundations-a-case-study-
approach&sa=D&sntz=1&usg=AOvVaw2Qxr7mG3gMjiA5NEeF-
stB)[**https://www.pirahansiah.com/topics/camera_calibration**](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh)
****
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##
Source code
Basic camear calibration source code by using OpenCV library in Jupyter
notebook
[https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ)
##
Reference
Semi-Auto Calibration for multi-camera system (Pirahansiah's method 2022) +
prognostic analysis [ using QR code in center of calibration pattern with four
different colors in each courners of the QR code for show the direction which
use for sincronize the points for all cameras)
Book Chapter (Springer):
Camera Calibration and Video Stabilization Framework for Robot Localization
[https://link.springer.com/chapter/10.1007/978-3-030-74540-0_12](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-74540-0_12&sa=D&sntz=1&usg=AOvVaw2F-HQeuD0NJee8C7oGOCbN)
IEEE paper:
Pattern image significance for camera calibration
[https://ieeexplore.ieee.org/abstract/document/8305440](https://www.google.com/url?q=https%3A%2F%2Fieeexplore.ieee.org%2Fabstract%2Fdocument%2F8305440&sa=D&sntz=1&usg=AOvVaw1BVeY_8PWNRXlfb4hlzjyi)
Camera calibration for multi-modal robot vision based on image quality
assessment [https://www.researchgate.net/profile/Farshid-
Pirahansiah/publication/288174690_Camera_calibration_for_multi-
modal_robot_vision_based_on_image_quality_assessment/links/5735bc2908aea45ee83c999e/Camera-
calibration-for-multi-modal-robot-vision-based-on-image-quality-
assessment.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.researchgate.net%2Fprofile%2FFarshid-
Pirahansiah%2Fpublication%2F288174690_Camera_calibration_for_multi-
modal_robot_vision_based_on_image_quality_assessment%2Flinks%2F5735bc2908aea45ee83c999e%2FCamera-
calibration-for-multi-modal-robot-vision-based-on-image-quality-
assessment.pdf&sa=D&sntz=1&usg=AOvVaw3OH6mE5ODgRSkTmNTsNpvh)
Part 3.
Basic of camera calibration + source code (Python+OpenCV)
[https://www.pirahansiah.com/topics/camera_calibration](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh)
Geometric camera calibration, also referred to as camera re-sectioning,
estimates the parameters of a lens and image sensor of an image or video
camera. These parameters can be used to correct for lens distortion, measure
the size of an object in world units, or determine the location of the camera
in a scene. These tasks are used in applications such as machine vision to
detect and measure objects. They are also used in robotics, navigation
systems, and 3-D scene reconstruction. Without any knowledge of the
calibration of the cameras, it is impossible to do better than projective
reconstruction (MathWorks).
Non-intrusive scene measurement tasks, such as 3D reconstruction, object
inspection, target or self-localization or scene mapping require a calibrated
camera model (Orghidan et al. 2011). Camera calibration is the process of
approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995;
Heikkila & Silven 1997) of a given photograph or video.
There are four main categories of camera calibration methods whereby a number
of algorithms have been proposed for each categories/methods, namely knowing
object based camera calibration, semi auto calibration, camera self-
calibration method, and camera calibration method based on active vision.
[https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ)
#camera_calibration #3D #multi_camera_calibration #extended_reality
#mixed_reality
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# Camera_Calibration
Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended
reality/mixed reality) 3D Image Processing with Deep Learning
introduction
Source code
Reference
#
Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended
reality/mixed reality) 3D Image Processing with Deep Learning
##
introduction
Geometric camera calibration, also referred to as camera re-sectioning,
estimates the parameters of a lens and image sensor of an image or video
camera. These parameters can be used to correct for lens distortion, measure
the size of an object in world units, or determine the location of the camera
in a scene. These tasks are used in applications such as machine vision to
detect and measure objects. They are also used in robotics, navigation
systems, and 3-D scene reconstruction. Without any knowledge of the
calibration of the cameras, it is impossible to do better than projective
reconstruction (MathWorks).
Non-intrusive scene measurement tasks, such as 3D reconstruction, object
inspection, target or self-localization or scene mapping require a calibrated
camera model (Orghidan et al. 2011). Camera calibration is the process of
approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995;
Heikkila & Silven 1997) of a given photograph or video.
There are four main categories of camera calibration methods whereby a number
of algorithms have been proposed for each categories/methods, namely knowing
object based camera calibration, semi auto calibration, camera self-
calibration method, and camera calibration method based on active vision.
In computer vision methods, image information from cameras can yield geometric
information pertaining to three-dimensional objects. Non-intrusive scene
measurement tasks, such as 3D reconstruction, object inspection, target or
self-localization, or scene mapping require a calibrated camera model
(Orghidan et al. 2011). The correlation between the geographical point and
camera image pixel is necessary for camera calibration. Hence, the camera’s
parameter, which constitutes the geometric model of camera imaging, are
utilized to establish the correlation between the three-dimensional geometric
location of one point and a corresponding point in an image (Wang et al.
2010). Typically, experiments are conducted to attain the aforementioned
parameters and relevant calculation, which is a process called camera
calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014;
Navarro et al. 2014).
Image information from cameras can be used to elucidate the geometric
information of a 3D object. The process of estimating the parameters of a
pinhole camera model is called camera calibration. The more accurate the
estimated parameters, the better the compensation that can be performed for
the next stage of the application. In the data collection stage, a camera will
take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila &
Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of
pair images from both cameras via high quality images and increased range of
slope of calibration pattern. The current methods simply create images upon
the detection of calibration pattern. Nonetheless, the consensus in literature
is that accurate camera calibration necessitates pure rotation (Zhang et al.
2008) and require sharp images. Recent breakthrough methods, such as Zhang’s
(Zhengyou 2000), use fixed threshold to elucidate pixel difference between the
frames and pre-setting variables, where slope information for image frame
selection in camera calibration phase has been neglected (Audet & Okutomi
2009). Conversely, these approaches become less reliable when image frames are
blurred. These problems necessitates that the camera calibration algorithm be
enhanced (Wang et al. 2010).
OpenCV
Deep Learning
[
**https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx-
QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg-
zy1CXgeEwRHbfcCHeA=w1280**](https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx-
QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg-
zy1CXgeEwRHbfcCHeA=w1280) ****
**Engineering of Camera Calibration**
Occasionally the out-of-the-box solution does not work, and you need some
modified version of the algorithms.
The first step of camera calibration is using known pattern images, such as
chessboard. However, sometimes the image quality and pattern are not match
with standard approach of calibration process.
I use some other technique to enhance the result. In the first step, we need
to improve the corner detection, and it may be done by fallowing steps.
* The chessboard is used as a pattern of alternating black and white squares,
\- which ensures that there is no bias toward one side or the other in
measurement.
* The image must be an grayscale (single-channel) image.
\- img - Input image. It should be grayscale and float32 type.
* gradianet x and y direction together (for better detection)
\- cv.morphologyEx( src, op, kernel[, dst[, anchor[, iterations[, borderType[,
borderValue]]]]] ) -> dst # different kernel is required
* using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities.
\- cv.cornerHarris( src, blockSize, ksize, k[, dst[, borderType]] ) -> dst #
the parameters a and b and c should be modified
> img - Input image. It should be grayscale and float32 type.
> blockSize - It is the size of neighborhood considered for corner detection
> ksize - Aperture parameter of the Sobel derivative used.
> k - Harris detector free parameter in the equation.
* contours to remove some noise:
- cv.connectedComponentsWithStats( image[, labels[, stats[, centroids[, connectivity[, ltype]]]]] ) -> retval, labels, stats, centroids
* subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates
cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) -> corners
\- image Input single-channel, 8-bit or float image.
\- corners Initial coordinates of the input corners and refined coordinates
provided for output.
\- winSize Half of the side length of the search window. (5*5 will be 11)
\- zeroZone It is used sometimes to avoid possible singularities of the auto
correlation matrix.
\- criteria Criteria for termination of the iterative process of corner
refinement.
* remove duplicate corners: for example corners are in less than 5 pixels should be remove
Reference:
[https://theailearner.com/tag/cv2-cornersubpix/](https://www.google.com/url?q=https%3A%2F%2Ftheailearner.com%2Ftag%2Fcv2-cornersubpix%2F&sa=D&sntz=1&usg=AOvVaw1LDrIDpdKUACBUnVjQPB5i)
[https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fdc%2Fd0d%2Ftutorial_py_features_harris.html&sa=D&sntz=1&usg=AOvVaw28cWci42D6B_nRD0F_RXjJ)
#Camera_Calibration #Camera-resectioning
See more:[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine-
learning-specialization%2Fmachine-learning-foundations-a-case-study-
approach&sa=D&sntz=1&usg=AOvVaw2Qxr7mG3gMjiA5NEeF-
stB)[**https://www.pirahansiah.com/topics/camera_calibration**](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh)
****
If you found the content informative, you may Follow me by
[LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j),
[twitter](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC),
for more!
**#FarshidPirahanSiah #pirahansiah**
##
Source code
Basic camear calibration source code by using OpenCV library in Jupyter
notebook
[https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ)
##
Reference
Semi-Auto Calibration for multi-camera system (Pirahansiah's method 2022) +
prognostic analysis [ using QR code in center of calibration pattern with four
different colors in each courners of the QR code for show the direction which
use for sincronize the points for all cameras)
Book Chapter (Springer):
Camera Calibration and Video Stabilization Framework for Robot Localization
[https://link.springer.com/chapter/10.1007/978-3-030-74540-0_12](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-74540-0_12&sa=D&sntz=1&usg=AOvVaw2F-HQeuD0NJee8C7oGOCbN)
IEEE paper:
Pattern image significance for camera calibration
[https://ieeexplore.ieee.org/abstract/document/8305440](https://www.google.com/url?q=https%3A%2F%2Fieeexplore.ieee.org%2Fabstract%2Fdocument%2F8305440&sa=D&sntz=1&usg=AOvVaw1BVeY_8PWNRXlfb4hlzjyi)
Camera calibration for multi-modal robot vision based on image quality
assessment [https://www.researchgate.net/profile/Farshid-
Pirahansiah/publication/288174690_Camera_calibration_for_multi-
modal_robot_vision_based_on_image_quality_assessment/links/5735bc2908aea45ee83c999e/Camera-
calibration-for-multi-modal-robot-vision-based-on-image-quality-
assessment.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.researchgate.net%2Fprofile%2FFarshid-
Pirahansiah%2Fpublication%2F288174690_Camera_calibration_for_multi-
modal_robot_vision_based_on_image_quality_assessment%2Flinks%2F5735bc2908aea45ee83c999e%2FCamera-
calibration-for-multi-modal-robot-vision-based-on-image-quality-
assessment.pdf&sa=D&sntz=1&usg=AOvVaw3OH6mE5ODgRSkTmNTsNpvh)
Part 3.
Basic of camera calibration + source code (Python+OpenCV)
[https://www.pirahansiah.com/topics/camera_calibration](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh)
Geometric camera calibration, also referred to as camera re-sectioning,
estimates the parameters of a lens and image sensor of an image or video
camera. These parameters can be used to correct for lens distortion, measure
the size of an object in world units, or determine the location of the camera
in a scene. These tasks are used in applications such as machine vision to
detect and measure objects. They are also used in robotics, navigation
systems, and 3-D scene reconstruction. Without any knowledge of the
calibration of the cameras, it is impossible to do better than projective
reconstruction (MathWorks).
Non-intrusive scene measurement tasks, such as 3D reconstruction, object
inspection, target or self-localization or scene mapping require a calibrated
camera model (Orghidan et al. 2011). Camera calibration is the process of
approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995;
Heikkila & Silven 1997) of a given photograph or video.
There are four main categories of camera calibration methods whereby a number
of algorithms have been proposed for each categories/methods, namely knowing
object based camera calibration, semi auto calibration, camera self-
calibration method, and camera calibration method based on active vision.
[https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ)
#camera_calibration #3D #multi_camera_calibration #extended_reality
#mixed_reality
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# Camera_Calibration
Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended
reality/mixed reality) 3D Image Processing with Deep Learning
introduction
Source code
Reference
#
Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended
reality/mixed reality) 3D Image Processing with Deep Learning
##
introduction
Geometric camera calibration, also referred to as camera re-sectioning,
estimates the parameters of a lens and image sensor of an image or video
camera. These parameters can be used to correct for lens distortion, measure
the size of an object in world units, or determine the location of the camera
in a scene. These tasks are used in applications such as machine vision to
detect and measure objects. They are also used in robotics, navigation
systems, and 3-D scene reconstruction. Without any knowledge of the
calibration of the cameras, it is impossible to do better than projective
reconstruction (MathWorks).
Non-intrusive scene measurement tasks, such as 3D reconstruction, object
inspection, target or self-localization or scene mapping require a calibrated
camera model (Orghidan et al. 2011). Camera calibration is the process of
approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995;
Heikkila & Silven 1997) of a given photograph or video.
There are four main categories of camera calibration methods whereby a number
of algorithms have been proposed for each categories/methods, namely knowing
object based camera calibration, semi auto calibration, camera self-
calibration method, and camera calibration method based on active vision.
In computer vision methods, image information from cameras can yield geometric
information pertaining to three-dimensional objects. Non-intrusive scene
measurement tasks, such as 3D reconstruction, object inspection, target or
self-localization, or scene mapping require a calibrated camera model
(Orghidan et al. 2011). The correlation between the geographical point and
camera image pixel is necessary for camera calibration. Hence, the camera’s
parameter, which constitutes the geometric model of camera imaging, are
utilized to establish the correlation between the three-dimensional geometric
location of one point and a corresponding point in an image (Wang et al.
2010). Typically, experiments are conducted to attain the aforementioned
parameters and relevant calculation, which is a process called camera
calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014;
Navarro et al. 2014).
Image information from cameras can be used to elucidate the geometric
information of a 3D object. The process of estimating the parameters of a
pinhole camera model is called camera calibration. The more accurate the
estimated parameters, the better the compensation that can be performed for
the next stage of the application. In the data collection stage, a camera will
take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila &
Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of
pair images from both cameras via high quality images and increased range of
slope of calibration pattern. The current methods simply create images upon
the detection of calibration pattern. Nonetheless, the consensus in literature
is that accurate camera calibration necessitates pure rotation (Zhang et al.
2008) and require sharp images. Recent breakthrough methods, such as Zhang’s
(Zhengyou 2000), use fixed threshold to elucidate pixel difference between the
frames and pre-setting variables, where slope information for image frame
selection in camera calibration phase has been neglected (Audet & Okutomi
2009). Conversely, these approaches become less reliable when image frames are
blurred. These problems necessitates that the camera calibration algorithm be
enhanced (Wang et al. 2010).
OpenCV
Deep Learning
[
**https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx-
QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg-
zy1CXgeEwRHbfcCHeA=w1280**](https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx-
QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg-
zy1CXgeEwRHbfcCHeA=w1280) ****
**Engineering of Camera Calibration**
Occasionally the out-of-the-box solution does not work, and you need some
modified version of the algorithms.
The first step of camera calibration is using known pattern images, such as
chessboard. However, sometimes the image quality and pattern are not match
with standard approach of calibration process.
I use some other technique to enhance the result. In the first step, we need
to improve the corner detection, and it may be done by fallowing steps.
* The chessboard is used as a pattern of alternating black and white squares,
\- which ensures that there is no bias toward one side or the other in
measurement.
* The image must be an grayscale (single-channel) image.
\- img - Input image. It should be grayscale and float32 type.
* gradianet x and y direction together (for better detection)
\- cv.morphologyEx( src, op, kernel[, dst[, anchor[, iterations[, borderType[,
borderValue]]]]] ) -> dst # different kernel is required
* using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities.
\- cv.cornerHarris( src, blockSize, ksize, k[, dst[, borderType]] ) -> dst #
the parameters a and b and c should be modified
> img - Input image. It should be grayscale and float32 type.
> blockSize - It is the size of neighborhood considered for corner detection
> ksize - Aperture parameter of the Sobel derivative used.
> k - Harris detector free parameter in the equation.
* contours to remove some noise:
- cv.connectedComponentsWithStats( image[, labels[, stats[, centroids[, connectivity[, ltype]]]]] ) -> retval, labels, stats, centroids
* subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates
cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) -> corners
\- image Input single-channel, 8-bit or float image.
\- corners Initial coordinates of the input corners and refined coordinates
provided for output.
\- winSize Half of the side length of the search window. (5*5 will be 11)
\- zeroZone It is used sometimes to avoid possible singularities of the auto
correlation matrix.
\- criteria Criteria for termination of the iterative process of corner
refinement.
* remove duplicate corners: for example corners are in less than 5 pixels should be remove
Reference:
[https://theailearner.com/tag/cv2-cornersubpix/](https://www.google.com/url?q=https%3A%2F%2Ftheailearner.com%2Ftag%2Fcv2-cornersubpix%2F&sa=D&sntz=1&usg=AOvVaw1LDrIDpdKUACBUnVjQPB5i)
[https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fdc%2Fd0d%2Ftutorial_py_features_harris.html&sa=D&sntz=1&usg=AOvVaw28cWci42D6B_nRD0F_RXjJ)
#Camera_Calibration #Camera-resectioning
See more:[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine-
learning-specialization%2Fmachine-learning-foundations-a-case-study-
approach&sa=D&sntz=1&usg=AOvVaw2Qxr7mG3gMjiA5NEeF-
stB)[**https://www.pirahansiah.com/topics/camera_calibration**](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh)
****
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[LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j),
[twitter](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC),
for more!
**#FarshidPirahanSiah #pirahansiah**
##
Source code
Basic camear calibration source code by using OpenCV library in Jupyter
notebook
[https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ)
##
Reference
Semi-Auto Calibration for multi-camera system (Pirahansiah's method 2022) +
prognostic analysis [ using QR code in center of calibration pattern with four
different colors in each courners of the QR code for show the direction which
use for sincronize the points for all cameras)
Book Chapter (Springer):
Camera Calibration and Video Stabilization Framework for Robot Localization
[https://link.springer.com/chapter/10.1007/978-3-030-74540-0_12](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-74540-0_12&sa=D&sntz=1&usg=AOvVaw2F-HQeuD0NJee8C7oGOCbN)
IEEE paper:
Pattern image significance for camera calibration
[https://ieeexplore.ieee.org/abstract/document/8305440](https://www.google.com/url?q=https%3A%2F%2Fieeexplore.ieee.org%2Fabstract%2Fdocument%2F8305440&sa=D&sntz=1&usg=AOvVaw1BVeY_8PWNRXlfb4hlzjyi)
Camera calibration for multi-modal robot vision based on image quality
assessment [https://www.researchgate.net/profile/Farshid-
Pirahansiah/publication/288174690_Camera_calibration_for_multi-
modal_robot_vision_based_on_image_quality_assessment/links/5735bc2908aea45ee83c999e/Camera-
calibration-for-multi-modal-robot-vision-based-on-image-quality-
assessment.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.researchgate.net%2Fprofile%2FFarshid-
Pirahansiah%2Fpublication%2F288174690_Camera_calibration_for_multi-
modal_robot_vision_based_on_image_quality_assessment%2Flinks%2F5735bc2908aea45ee83c999e%2FCamera-
calibration-for-multi-modal-robot-vision-based-on-image-quality-
assessment.pdf&sa=D&sntz=1&usg=AOvVaw3OH6mE5ODgRSkTmNTsNpvh)
Part 3.
Basic of camera calibration + source code (Python+OpenCV)
[https://www.pirahansiah.com/topics/camera_calibration](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh)
Geometric camera calibration, also referred to as camera re-sectioning,
estimates the parameters of a lens and image sensor of an image or video
camera. These parameters can be used to correct for lens distortion, measure
the size of an object in world units, or determine the location of the camera
in a scene. These tasks are used in applications such as machine vision to
detect and measure objects. They are also used in robotics, navigation
systems, and 3-D scene reconstruction. Without any knowledge of the
calibration of the cameras, it is impossible to do better than projective
reconstruction (MathWorks).
Non-intrusive scene measurement tasks, such as 3D reconstruction, object
inspection, target or self-localization or scene mapping require a calibrated
camera model (Orghidan et al. 2011). Camera calibration is the process of
approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995;
Heikkila & Silven 1997) of a given photograph or video.
There are four main categories of camera calibration methods whereby a number
of algorithms have been proposed for each categories/methods, namely knowing
object based camera calibration, semi auto calibration, camera self-
calibration method, and camera calibration method based on active vision.
[https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ)
#camera_calibration #3D #multi_camera_calibration #extended_reality
#mixed_reality
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# Camera_Calibration
Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended
reality/mixed reality) 3D Image Processing with Deep Learning
introduction
Source code
Reference
#
Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended
reality/mixed reality) 3D Image Processing with Deep Learning
##
introduction
Geometric camera calibration, also referred to as camera re-sectioning,
estimates the parameters of a lens and image sensor of an image or video
camera. These parameters can be used to correct for lens distortion, measure
the size of an object in world units, or determine the location of the camera
in a scene. These tasks are used in applications such as machine vision to
detect and measure objects. They are also used in robotics, navigation
systems, and 3-D scene reconstruction. Without any knowledge of the
calibration of the cameras, it is impossible to do better than projective
reconstruction (MathWorks).
Non-intrusive scene measurement tasks, such as 3D reconstruction, object
inspection, target or self-localization or scene mapping require a calibrated
camera model (Orghidan et al. 2011). Camera calibration is the process of
approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995;
Heikkila & Silven 1997) of a given photograph or video.
There are four main categories of camera calibration methods whereby a number
of algorithms have been proposed for each categories/methods, namely knowing
object based camera calibration, semi auto calibration, camera self-
calibration method, and camera calibration method based on active vision.
In computer vision methods, image information from cameras can yield geometric
information pertaining to three-dimensional objects. Non-intrusive scene
measurement tasks, such as 3D reconstruction, object inspection, target or
self-localization, or scene mapping require a calibrated camera model
(Orghidan et al. 2011). The correlation between the geographical point and
camera image pixel is necessary for camera calibration. Hence, the camera’s
parameter, which constitutes the geometric model of camera imaging, are
utilized to establish the correlation between the three-dimensional geometric
location of one point and a corresponding point in an image (Wang et al.
2010). Typically, experiments are conducted to attain the aforementioned
parameters and relevant calculation, which is a process called camera
calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014;
Navarro et al. 2014).
Image information from cameras can be used to elucidate the geometric
information of a 3D object. The process of estimating the parameters of a
pinhole camera model is called camera calibration. The more accurate the
estimated parameters, the better the compensation that can be performed for
the next stage of the application. In the data collection stage, a camera will
take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila &
Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of
pair images from both cameras via high quality images and increased range of
slope of calibration pattern. The current methods simply create images upon
the detection of calibration pattern. Nonetheless, the consensus in literature
is that accurate camera calibration necessitates pure rotation (Zhang et al.
2008) and require sharp images. Recent breakthrough methods, such as Zhang’s
(Zhengyou 2000), use fixed threshold to elucidate pixel difference between the
frames and pre-setting variables, where slope information for image frame
selection in camera calibration phase has been neglected (Audet & Okutomi
2009). Conversely, these approaches become less reliable when image frames are
blurred. These problems necessitates that the camera calibration algorithm be
enhanced (Wang et al. 2010).
OpenCV
Deep Learning
[
**https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx-
QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg-
zy1CXgeEwRHbfcCHeA=w1280**](https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx-
QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg-
zy1CXgeEwRHbfcCHeA=w1280) ****
**Engineering of Camera Calibration**
Occasionally the out-of-the-box solution does not work, and you need some
modified version of the algorithms.
The first step of camera calibration is using known pattern images, such as
chessboard. However, sometimes the image quality and pattern are not match
with standard approach of calibration process.
I use some other technique to enhance the result. In the first step, we need
to improve the corner detection, and it may be done by fallowing steps.
* The chessboard is used as a pattern of alternating black and white squares,
\- which ensures that there is no bias toward one side or the other in
measurement.
* The image must be an grayscale (single-channel) image.
\- img - Input image. It should be grayscale and float32 type.
* gradianet x and y direction together (for better detection)
\- cv.morphologyEx( src, op, kernel[, dst[, anchor[, iterations[, borderType[,
borderValue]]]]] ) -> dst # different kernel is required
* using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities.
\- cv.cornerHarris( src, blockSize, ksize, k[, dst[, borderType]] ) -> dst #
the parameters a and b and c should be modified
> img - Input image. It should be grayscale and float32 type.
> blockSize - It is the size of neighborhood considered for corner detection
> ksize - Aperture parameter of the Sobel derivative used.
> k - Harris detector free parameter in the equation.
* contours to remove some noise:
- cv.connectedComponentsWithStats( image[, labels[, stats[, centroids[, connectivity[, ltype]]]]] ) -> retval, labels, stats, centroids
* subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates
cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) -> corners
\- image Input single-channel, 8-bit or float image.
\- corners Initial coordinates of the input corners and refined coordinates
provided for output.
\- winSize Half of the side length of the search window. (5*5 will be 11)
\- zeroZone It is used sometimes to avoid possible singularities of the auto
correlation matrix.
\- criteria Criteria for termination of the iterative process of corner
refinement.
* remove duplicate corners: for example corners are in less than 5 pixels should be remove
Reference:
[https://theailearner.com/tag/cv2-cornersubpix/](https://www.google.com/url?q=https%3A%2F%2Ftheailearner.com%2Ftag%2Fcv2-cornersubpix%2F&sa=D&sntz=1&usg=AOvVaw1LDrIDpdKUACBUnVjQPB5i)
[https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fdc%2Fd0d%2Ftutorial_py_features_harris.html&sa=D&sntz=1&usg=AOvVaw28cWci42D6B_nRD0F_RXjJ)
#Camera_Calibration #Camera-resectioning
See more:[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine-
learning-specialization%2Fmachine-learning-foundations-a-case-study-
approach&sa=D&sntz=1&usg=AOvVaw2Qxr7mG3gMjiA5NEeF-
stB)[**https://www.pirahansiah.com/topics/camera_calibration**](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh)
****
If you found the content informative, you may Follow me by
[LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j),
[twitter](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC),
for more!
**#FarshidPirahanSiah #pirahansiah**
##
Source code
Basic camear calibration source code by using OpenCV library in Jupyter
notebook
[https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ)
##
Reference
Semi-Auto Calibration for multi-camera system (Pirahansiah's method 2022) +
prognostic analysis [ using QR code in center of calibration pattern with four
different colors in each courners of the QR code for show the direction which
use for sincronize the points for all cameras)
Book Chapter (Springer):
Camera Calibration and Video Stabilization Framework for Robot Localization
[https://link.springer.com/chapter/10.1007/978-3-030-74540-0_12](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-74540-0_12&sa=D&sntz=1&usg=AOvVaw2F-HQeuD0NJee8C7oGOCbN)
IEEE paper:
Pattern image significance for camera calibration
[https://ieeexplore.ieee.org/abstract/document/8305440](https://www.google.com/url?q=https%3A%2F%2Fieeexplore.ieee.org%2Fabstract%2Fdocument%2F8305440&sa=D&sntz=1&usg=AOvVaw1BVeY_8PWNRXlfb4hlzjyi)
Camera calibration for multi-modal robot vision based on image quality
assessment [https://www.researchgate.net/profile/Farshid-
Pirahansiah/publication/288174690_Camera_calibration_for_multi-
modal_robot_vision_based_on_image_quality_assessment/links/5735bc2908aea45ee83c999e/Camera-
calibration-for-multi-modal-robot-vision-based-on-image-quality-
assessment.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.researchgate.net%2Fprofile%2FFarshid-
Pirahansiah%2Fpublication%2F288174690_Camera_calibration_for_multi-
modal_robot_vision_based_on_image_quality_assessment%2Flinks%2F5735bc2908aea45ee83c999e%2FCamera-
calibration-for-multi-modal-robot-vision-based-on-image-quality-
assessment.pdf&sa=D&sntz=1&usg=AOvVaw3OH6mE5ODgRSkTmNTsNpvh)
Part 3.
Basic of camera calibration + source code (Python+OpenCV)
[https://www.pirahansiah.com/topics/camera_calibration](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh)
Geometric camera calibration, also referred to as camera re-sectioning,
estimates the parameters of a lens and image sensor of an image or video
camera. These parameters can be used to correct for lens distortion, measure
the size of an object in world units, or determine the location of the camera
in a scene. These tasks are used in applications such as machine vision to
detect and measure objects. They are also used in robotics, navigation
systems, and 3-D scene reconstruction. Without any knowledge of the
calibration of the cameras, it is impossible to do better than projective
reconstruction (MathWorks).
Non-intrusive scene measurement tasks, such as 3D reconstruction, object
inspection, target or self-localization or scene mapping require a calibrated
camera model (Orghidan et al. 2011). Camera calibration is the process of
approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995;
Heikkila & Silven 1997) of a given photograph or video.
There are four main categories of camera calibration methods whereby a number
of algorithms have been proposed for each categories/methods, namely knowing
object based camera calibration, semi auto calibration, camera self-
calibration method, and camera calibration method based on active vision.
[https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ)
#camera_calibration #3D #multi_camera_calibration #extended_reality
#mixed_reality
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# Camera_Calibration
Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended
reality/mixed reality) 3D Image Processing with Deep Learning
introduction
Source code
Reference
#
Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended
reality/mixed reality) 3D Image Processing with Deep Learning
##
introduction
Geometric camera calibration, also referred to as camera re-sectioning,
estimates the parameters of a lens and image sensor of an image or video
camera. These parameters can be used to correct for lens distortion, measure
the size of an object in world units, or determine the location of the camera
in a scene. These tasks are used in applications such as machine vision to
detect and measure objects. They are also used in robotics, navigation
systems, and 3-D scene reconstruction. Without any knowledge of the
calibration of the cameras, it is impossible to do better than projective
reconstruction (MathWorks).
Non-intrusive scene measurement tasks, such as 3D reconstruction, object
inspection, target or self-localization or scene mapping require a calibrated
camera model (Orghidan et al. 2011). Camera calibration is the process of
approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995;
Heikkila & Silven 1997) of a given photograph or video.
There are four main categories of camera calibration methods whereby a number
of algorithms have been proposed for each categories/methods, namely knowing
object based camera calibration, semi auto calibration, camera self-
calibration method, and camera calibration method based on active vision.
In computer vision methods, image information from cameras can yield geometric
information pertaining to three-dimensional objects. Non-intrusive scene
measurement tasks, such as 3D reconstruction, object inspection, target or
self-localization, or scene mapping require a calibrated camera model
(Orghidan et al. 2011). The correlation between the geographical point and
camera image pixel is necessary for camera calibration. Hence, the camera’s
parameter, which constitutes the geometric model of camera imaging, are
utilized to establish the correlation between the three-dimensional geometric
location of one point and a corresponding point in an image (Wang et al.
2010). Typically, experiments are conducted to attain the aforementioned
parameters and relevant calculation, which is a process called camera
calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014;
Navarro et al. 2014).
Image information from cameras can be used to elucidate the geometric
information of a 3D object. The process of estimating the parameters of a
pinhole camera model is called camera calibration. The more accurate the
estimated parameters, the better the compensation that can be performed for
the next stage of the application. In the data collection stage, a camera will
take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila &
Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of
pair images from both cameras via high quality images and increased range of
slope of calibration pattern. The current methods simply create images upon
the detection of calibration pattern. Nonetheless, the consensus in literature
is that accurate camera calibration necessitates pure rotation (Zhang et al.
2008) and require sharp images. Recent breakthrough methods, such as Zhang’s
(Zhengyou 2000), use fixed threshold to elucidate pixel difference between the
frames and pre-setting variables, where slope information for image frame
selection in camera calibration phase has been neglected (Audet & Okutomi
2009). Conversely, these approaches become less reliable when image frames are
blurred. These problems necessitates that the camera calibration algorithm be
enhanced (Wang et al. 2010).
OpenCV
Deep Learning
[
**https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx-
QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg-
zy1CXgeEwRHbfcCHeA=w1280**](https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx-
QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg-
zy1CXgeEwRHbfcCHeA=w1280) ****
**Engineering of Camera Calibration**
Occasionally the out-of-the-box solution does not work, and you need some
modified version of the algorithms.
The first step of camera calibration is using known pattern images, such as
chessboard. However, sometimes the image quality and pattern are not match
with standard approach of calibration process.
I use some other technique to enhance the result. In the first step, we need
to improve the corner detection, and it may be done by fallowing steps.
* The chessboard is used as a pattern of alternating black and white squares,
\- which ensures that there is no bias toward one side or the other in
measurement.
* The image must be an grayscale (single-channel) image.
\- img - Input image. It should be grayscale and float32 type.
* gradianet x and y direction together (for better detection)
\- cv.morphologyEx( src, op, kernel[, dst[, anchor[, iterations[, borderType[,
borderValue]]]]] ) -> dst # different kernel is required
* using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities.
\- cv.cornerHarris( src, blockSize, ksize, k[, dst[, borderType]] ) -> dst #
the parameters a and b and c should be modified
> img - Input image. It should be grayscale and float32 type.
> blockSize - It is the size of neighborhood considered for corner detection
> ksize - Aperture parameter of the Sobel derivative used.
> k - Harris detector free parameter in the equation.
* contours to remove some noise:
- cv.connectedComponentsWithStats( image[, labels[, stats[, centroids[, connectivity[, ltype]]]]] ) -> retval, labels, stats, centroids
* subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates
cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) -> corners
\- image Input single-channel, 8-bit or float image.
\- corners Initial coordinates of the input corners and refined coordinates
provided for output.
\- winSize Half of the side length of the search window. (5*5 will be 11)
\- zeroZone It is used sometimes to avoid possible singularities of the auto
correlation matrix.
\- criteria Criteria for termination of the iterative process of corner
refinement.
* remove duplicate corners: for example corners are in less than 5 pixels should be remove
Reference:
[https://theailearner.com/tag/cv2-cornersubpix/](https://www.google.com/url?q=https%3A%2F%2Ftheailearner.com%2Ftag%2Fcv2-cornersubpix%2F&sa=D&sntz=1&usg=AOvVaw1LDrIDpdKUACBUnVjQPB5i)
[https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fdc%2Fd0d%2Ftutorial_py_features_harris.html&sa=D&sntz=1&usg=AOvVaw28cWci42D6B_nRD0F_RXjJ)
#Camera_Calibration #Camera-resectioning
See more:[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine-
learning-specialization%2Fmachine-learning-foundations-a-case-study-
approach&sa=D&sntz=1&usg=AOvVaw2Qxr7mG3gMjiA5NEeF-
stB)[**https://www.pirahansiah.com/topics/camera_calibration**](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh)
****
If you found the content informative, you may Follow me by
[LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j),
[twitter](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC),
for more!
**#FarshidPirahanSiah #pirahansiah**
##
Source code
Basic camear calibration source code by using OpenCV library in Jupyter
notebook
[https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ)
##
Reference
Semi-Auto Calibration for multi-camera system (Pirahansiah's method 2022) +
prognostic analysis [ using QR code in center of calibration pattern with four
different colors in each courners of the QR code for show the direction which
use for sincronize the points for all cameras)
Book Chapter (Springer):
Camera Calibration and Video Stabilization Framework for Robot Localization
[https://link.springer.com/chapter/10.1007/978-3-030-74540-0_12](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-74540-0_12&sa=D&sntz=1&usg=AOvVaw2F-HQeuD0NJee8C7oGOCbN)
IEEE paper:
Pattern image significance for camera calibration
[https://ieeexplore.ieee.org/abstract/document/8305440](https://www.google.com/url?q=https%3A%2F%2Fieeexplore.ieee.org%2Fabstract%2Fdocument%2F8305440&sa=D&sntz=1&usg=AOvVaw1BVeY_8PWNRXlfb4hlzjyi)
Camera calibration for multi-modal robot vision based on image quality
assessment [https://www.researchgate.net/profile/Farshid-
Pirahansiah/publication/288174690_Camera_calibration_for_multi-
modal_robot_vision_based_on_image_quality_assessment/links/5735bc2908aea45ee83c999e/Camera-
calibration-for-multi-modal-robot-vision-based-on-image-quality-
assessment.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.researchgate.net%2Fprofile%2FFarshid-
Pirahansiah%2Fpublication%2F288174690_Camera_calibration_for_multi-
modal_robot_vision_based_on_image_quality_assessment%2Flinks%2F5735bc2908aea45ee83c999e%2FCamera-
calibration-for-multi-modal-robot-vision-based-on-image-quality-
assessment.pdf&sa=D&sntz=1&usg=AOvVaw3OH6mE5ODgRSkTmNTsNpvh)
Part 3.
Basic of camera calibration + source code (Python+OpenCV)
[https://www.pirahansiah.com/topics/camera_calibration](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh)
Geometric camera calibration, also referred to as camera re-sectioning,
estimates the parameters of a lens and image sensor of an image or video
camera. These parameters can be used to correct for lens distortion, measure
the size of an object in world units, or determine the location of the camera
in a scene. These tasks are used in applications such as machine vision to
detect and measure objects. They are also used in robotics, navigation
systems, and 3-D scene reconstruction. Without any knowledge of the
calibration of the cameras, it is impossible to do better than projective
reconstruction (MathWorks).
Non-intrusive scene measurement tasks, such as 3D reconstruction, object
inspection, target or self-localization or scene mapping require a calibrated
camera model (Orghidan et al. 2011). Camera calibration is the process of
approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995;
Heikkila & Silven 1997) of a given photograph or video.
There are four main categories of camera calibration methods whereby a number
of algorithms have been proposed for each categories/methods, namely knowing
object based camera calibration, semi auto calibration, camera self-
calibration method, and camera calibration method based on active vision.
[https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ)
#camera_calibration #3D #multi_camera_calibration #extended_reality
#mixed_reality
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# Video Tracking
Tracking
List of Datasets
Source code
Self collected datasets
Video labeling
Reference
The online course about multiple object tracking in Edx:
Resilient object detection and tracking on Edge and cloud (AWS):
The best methods of object tracking run on GPU. The versioning of different
deep learning frameworks are crucial. For example the latest version of OS for
Jetson Nano
[Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN-
Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we
need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 can process up to 15 FPS with Full HD
videos. Methods on tracking is very different. First generation, it is
completely based on computer vision. The second generation combining Kalman
filter and advanced computer vision (SIFT), the third generation using deep
learning and some of the methods of previous generation like Kalman filter.
The fourth generation using combination of two deep learning methods. And the
latest generation using complete end to end models like RNN. Object tracking
works with all combination of environments such as, moving objects, moving
objects and camera in dynamic environments. As long as object appear in the
frame until disappeared it the tracking can track and identification as one
objects. No mater how many FPS.
#
Tracking
* Classic object tracking
* * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection.
* Kalman filtering, sparse and dense optical flow,
* Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter
* SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results.
* The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis.
* Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences.
* Multi-object tracking datasets
* large-scale benchmark Multi-Class Multi-object tracking datasets
* VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking.
* the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue.
* Temporal coherence. A feasible way to exploit temporal coherence is using object trackers
* Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance.
* ###
List of Datasets
* **MOT20**
* KITTI Tracking
* MOTChallenge 2015
* UA-DETRAC Tracking
* DukeMTMC
* Campus
* MOT17
* UAVDT-MOT
* VisDrone
###
Source code
* ROLO
* TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX)
* SiamMask
* PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn)
* Deep SORT
* PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
* TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe)
* TrackR-CNN
* TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5)
* Tracktor++
* PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8)
* JDE
* PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO)
* [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn)
#
Self collected datasets
##
[Video
labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome-
data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc)
* [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy)
* [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r)
* [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64)
#
Reference
1. Vision Meets Drones: Past, Present and Future
2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
3.
4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R)
5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv)
6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8)
7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC)
8.
some examples
Endeavor to summarize MOT:
The best methods running on GPU. The versioning of different deep learning
frameworks are crucial. For example the latest version of OS for Jetson Nano
"Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need
to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 may run in real time.
Methods on tracking is very different. First generation, it is completely
based on computer vision. The second generation combining machine learning,
Kalman filter and advanced computer vision (SIFT), the third generation using
deep learning and some of the methods of previous generation like Kalman
filter. The fourth generation using combination of two deep learning methods.
And the latest generation using complete end to end models with RNN.
Object tracking works with all combination of environments such as, moving
objects, moving objects and camera in dynamic environments. As long as object
appear in the frame until disappeared it the tracking can track and
identification as one objects. No mater how many FPS.
In around 130 videos of the course of Multiple Object Tracking on EDEX means
this topic is huge and require more attention for the more research and
development.
Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is
arm based and not many package are build for it.
Datasets for Tracking:
MOTChallenge
MOT15
MOT16/17
MOT19
KITTI
UA-DETRAC tracking benchmark
_metrics_
* _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames.
* _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment.
* _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames.
_False trajectories_ : predicted trajectories which do not correspond to a
real object (i.e. to a ground truth trajectory).
* _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is
mistakenly changed.
Test:
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Only Ubuntu, Not mac, can based on GPU, webcam not working
[https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp)
Only GPU
YouTube:
OpenCV
[Tracking Objects | OpenCV Python Tutorials for Beginners
2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop-
RoboticsandAI)
Multiple Object Tracking
[Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and
Deep SORT [FULL
COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy)
There are at least 7 types of tracker algorithms that can be[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[
](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC):
not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
* MIL
* BOOSTING
* MEDIANFLOW
* TLD
* KCF
* GOTURN
* MOSSE
Kalman filtering, sparse and dense optical flow are[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK)[Simple Online and Realtime Tracking
(SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK), which uses a combination of the Hungarian algorithm and Kalman filter to
achieve decent object tracking.
R-CNN
around 2000 region proposals
[selective
search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7)
share colors and textures, lightning conditions
slow to train and test
Fast R-CNN
computes a convolutional feature map for the entire input image in a single
forward pass of the network
architecture is trained end-to-end with a multi-task loss
[https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
Simple Online and Realtime Tracking with a Deep Association Metric. 2017
[https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp)
[https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg)
#
[ **The online course about multiple object tracking in
Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti-
object-tracking-for-automotive-
systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql)
Course Section 0: Welcome and Introduction '
Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition
and definitions: 15 videos
[Introductory
examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk)
Is about the accurate perception of the driving environment
Avoid collisions at the airport
Crowd surveillance
Crowd behavior
Planning of emergency procedures
Pedestrian tracking using LIDAR
Tracking based on detections
Group behavior
Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23
videos
[Introduction to SOT in
Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc-
SidNL1kRF7)
Pruning and merging
Pruning : remove hypotheses with small weights (and renormalize)
Merging: approximate a mixture of densities by a single density (often
Gaussian)
Gating: technique to disregard unreasonable detections [pruning]
SOT
* Gaussian densities
* Nearest neighbour (NN) filter [pruning]
* Probabilistic data association (PDA) filter [merging]
* Gaussian mixture densites
* Gaussian sum filter (GSF) [pruning/merging]
Part 3: Tracking a known number of objects in clutter 30
3.3.6 Predicting the n object density
**3.4.1 Introduction to data association**
Part 4: Random Finite Sets 24
Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube]
Part 6: Outlook - what is next? 18 [only in YouTube]
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# Video Tracking
Tracking
List of Datasets
Source code
Self collected datasets
Video labeling
Reference
The online course about multiple object tracking in Edx:
Resilient object detection and tracking on Edge and cloud (AWS):
The best methods of object tracking run on GPU. The versioning of different
deep learning frameworks are crucial. For example the latest version of OS for
Jetson Nano
[Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN-
Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we
need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 can process up to 15 FPS with Full HD
videos. Methods on tracking is very different. First generation, it is
completely based on computer vision. The second generation combining Kalman
filter and advanced computer vision (SIFT), the third generation using deep
learning and some of the methods of previous generation like Kalman filter.
The fourth generation using combination of two deep learning methods. And the
latest generation using complete end to end models like RNN. Object tracking
works with all combination of environments such as, moving objects, moving
objects and camera in dynamic environments. As long as object appear in the
frame until disappeared it the tracking can track and identification as one
objects. No mater how many FPS.
#
Tracking
* Classic object tracking
* * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection.
* Kalman filtering, sparse and dense optical flow,
* Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter
* SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results.
* The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis.
* Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences.
* Multi-object tracking datasets
* large-scale benchmark Multi-Class Multi-object tracking datasets
* VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking.
* the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue.
* Temporal coherence. A feasible way to exploit temporal coherence is using object trackers
* Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance.
* ###
List of Datasets
* **MOT20**
* KITTI Tracking
* MOTChallenge 2015
* UA-DETRAC Tracking
* DukeMTMC
* Campus
* MOT17
* UAVDT-MOT
* VisDrone
###
Source code
* ROLO
* TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX)
* SiamMask
* PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn)
* Deep SORT
* PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
* TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe)
* TrackR-CNN
* TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5)
* Tracktor++
* PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8)
* JDE
* PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO)
* [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn)
#
Self collected datasets
##
[Video
labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome-
data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc)
* [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy)
* [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r)
* [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64)
#
Reference
1. Vision Meets Drones: Past, Present and Future
2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
3.
4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R)
5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv)
6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8)
7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC)
8.
some examples
Endeavor to summarize MOT:
The best methods running on GPU. The versioning of different deep learning
frameworks are crucial. For example the latest version of OS for Jetson Nano
"Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need
to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 may run in real time.
Methods on tracking is very different. First generation, it is completely
based on computer vision. The second generation combining machine learning,
Kalman filter and advanced computer vision (SIFT), the third generation using
deep learning and some of the methods of previous generation like Kalman
filter. The fourth generation using combination of two deep learning methods.
And the latest generation using complete end to end models with RNN.
Object tracking works with all combination of environments such as, moving
objects, moving objects and camera in dynamic environments. As long as object
appear in the frame until disappeared it the tracking can track and
identification as one objects. No mater how many FPS.
In around 130 videos of the course of Multiple Object Tracking on EDEX means
this topic is huge and require more attention for the more research and
development.
Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is
arm based and not many package are build for it.
Datasets for Tracking:
MOTChallenge
MOT15
MOT16/17
MOT19
KITTI
UA-DETRAC tracking benchmark
_metrics_
* _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames.
* _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment.
* _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames.
_False trajectories_ : predicted trajectories which do not correspond to a
real object (i.e. to a ground truth trajectory).
* _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is
mistakenly changed.
Test:
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Only Ubuntu, Not mac, can based on GPU, webcam not working
[https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp)
Only GPU
YouTube:
OpenCV
[Tracking Objects | OpenCV Python Tutorials for Beginners
2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop-
RoboticsandAI)
Multiple Object Tracking
[Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and
Deep SORT [FULL
COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy)
There are at least 7 types of tracker algorithms that can be[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[
](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC):
not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
* MIL
* BOOSTING
* MEDIANFLOW
* TLD
* KCF
* GOTURN
* MOSSE
Kalman filtering, sparse and dense optical flow are[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK)[Simple Online and Realtime Tracking
(SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK), which uses a combination of the Hungarian algorithm and Kalman filter to
achieve decent object tracking.
R-CNN
around 2000 region proposals
[selective
search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7)
share colors and textures, lightning conditions
slow to train and test
Fast R-CNN
computes a convolutional feature map for the entire input image in a single
forward pass of the network
architecture is trained end-to-end with a multi-task loss
[https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
Simple Online and Realtime Tracking with a Deep Association Metric. 2017
[https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp)
[https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg)
#
[ **The online course about multiple object tracking in
Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti-
object-tracking-for-automotive-
systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql)
Course Section 0: Welcome and Introduction '
Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition
and definitions: 15 videos
[Introductory
examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk)
Is about the accurate perception of the driving environment
Avoid collisions at the airport
Crowd surveillance
Crowd behavior
Planning of emergency procedures
Pedestrian tracking using LIDAR
Tracking based on detections
Group behavior
Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23
videos
[Introduction to SOT in
Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc-
SidNL1kRF7)
Pruning and merging
Pruning : remove hypotheses with small weights (and renormalize)
Merging: approximate a mixture of densities by a single density (often
Gaussian)
Gating: technique to disregard unreasonable detections [pruning]
SOT
* Gaussian densities
* Nearest neighbour (NN) filter [pruning]
* Probabilistic data association (PDA) filter [merging]
* Gaussian mixture densites
* Gaussian sum filter (GSF) [pruning/merging]
Part 3: Tracking a known number of objects in clutter 30
3.3.6 Predicting the n object density
**3.4.1 Introduction to data association**
Part 4: Random Finite Sets 24
Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube]
Part 6: Outlook - what is next? 18 [only in YouTube]
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# Video Tracking
Tracking
List of Datasets
Source code
Self collected datasets
Video labeling
Reference
The online course about multiple object tracking in Edx:
Resilient object detection and tracking on Edge and cloud (AWS):
The best methods of object tracking run on GPU. The versioning of different
deep learning frameworks are crucial. For example the latest version of OS for
Jetson Nano
[Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN-
Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we
need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 can process up to 15 FPS with Full HD
videos. Methods on tracking is very different. First generation, it is
completely based on computer vision. The second generation combining Kalman
filter and advanced computer vision (SIFT), the third generation using deep
learning and some of the methods of previous generation like Kalman filter.
The fourth generation using combination of two deep learning methods. And the
latest generation using complete end to end models like RNN. Object tracking
works with all combination of environments such as, moving objects, moving
objects and camera in dynamic environments. As long as object appear in the
frame until disappeared it the tracking can track and identification as one
objects. No mater how many FPS.
#
Tracking
* Classic object tracking
* * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection.
* Kalman filtering, sparse and dense optical flow,
* Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter
* SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results.
* The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis.
* Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences.
* Multi-object tracking datasets
* large-scale benchmark Multi-Class Multi-object tracking datasets
* VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking.
* the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue.
* Temporal coherence. A feasible way to exploit temporal coherence is using object trackers
* Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance.
* ###
List of Datasets
* **MOT20**
* KITTI Tracking
* MOTChallenge 2015
* UA-DETRAC Tracking
* DukeMTMC
* Campus
* MOT17
* UAVDT-MOT
* VisDrone
###
Source code
* ROLO
* TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX)
* SiamMask
* PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn)
* Deep SORT
* PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
* TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe)
* TrackR-CNN
* TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5)
* Tracktor++
* PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8)
* JDE
* PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO)
* [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn)
#
Self collected datasets
##
[Video
labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome-
data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc)
* [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy)
* [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r)
* [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64)
#
Reference
1. Vision Meets Drones: Past, Present and Future
2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
3.
4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R)
5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv)
6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8)
7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC)
8.
some examples
Endeavor to summarize MOT:
The best methods running on GPU. The versioning of different deep learning
frameworks are crucial. For example the latest version of OS for Jetson Nano
"Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need
to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 may run in real time.
Methods on tracking is very different. First generation, it is completely
based on computer vision. The second generation combining machine learning,
Kalman filter and advanced computer vision (SIFT), the third generation using
deep learning and some of the methods of previous generation like Kalman
filter. The fourth generation using combination of two deep learning methods.
And the latest generation using complete end to end models with RNN.
Object tracking works with all combination of environments such as, moving
objects, moving objects and camera in dynamic environments. As long as object
appear in the frame until disappeared it the tracking can track and
identification as one objects. No mater how many FPS.
In around 130 videos of the course of Multiple Object Tracking on EDEX means
this topic is huge and require more attention for the more research and
development.
Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is
arm based and not many package are build for it.
Datasets for Tracking:
MOTChallenge
MOT15
MOT16/17
MOT19
KITTI
UA-DETRAC tracking benchmark
_metrics_
* _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames.
* _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment.
* _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames.
_False trajectories_ : predicted trajectories which do not correspond to a
real object (i.e. to a ground truth trajectory).
* _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is
mistakenly changed.
Test:
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Only Ubuntu, Not mac, can based on GPU, webcam not working
[https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp)
Only GPU
YouTube:
OpenCV
[Tracking Objects | OpenCV Python Tutorials for Beginners
2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop-
RoboticsandAI)
Multiple Object Tracking
[Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and
Deep SORT [FULL
COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy)
There are at least 7 types of tracker algorithms that can be[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[
](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC):
not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
* MIL
* BOOSTING
* MEDIANFLOW
* TLD
* KCF
* GOTURN
* MOSSE
Kalman filtering, sparse and dense optical flow are[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK)[Simple Online and Realtime Tracking
(SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK), which uses a combination of the Hungarian algorithm and Kalman filter to
achieve decent object tracking.
R-CNN
around 2000 region proposals
[selective
search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7)
share colors and textures, lightning conditions
slow to train and test
Fast R-CNN
computes a convolutional feature map for the entire input image in a single
forward pass of the network
architecture is trained end-to-end with a multi-task loss
[https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
Simple Online and Realtime Tracking with a Deep Association Metric. 2017
[https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp)
[https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg)
#
[ **The online course about multiple object tracking in
Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti-
object-tracking-for-automotive-
systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql)
Course Section 0: Welcome and Introduction '
Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition
and definitions: 15 videos
[Introductory
examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk)
Is about the accurate perception of the driving environment
Avoid collisions at the airport
Crowd surveillance
Crowd behavior
Planning of emergency procedures
Pedestrian tracking using LIDAR
Tracking based on detections
Group behavior
Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23
videos
[Introduction to SOT in
Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc-
SidNL1kRF7)
Pruning and merging
Pruning : remove hypotheses with small weights (and renormalize)
Merging: approximate a mixture of densities by a single density (often
Gaussian)
Gating: technique to disregard unreasonable detections [pruning]
SOT
* Gaussian densities
* Nearest neighbour (NN) filter [pruning]
* Probabilistic data association (PDA) filter [merging]
* Gaussian mixture densites
* Gaussian sum filter (GSF) [pruning/merging]
Part 3: Tracking a known number of objects in clutter 30
3.3.6 Predicting the n object density
**3.4.1 Introduction to data association**
Part 4: Random Finite Sets 24
Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube]
Part 6: Outlook - what is next? 18 [only in YouTube]
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# Video Tracking
Tracking
List of Datasets
Source code
Self collected datasets
Video labeling
Reference
The online course about multiple object tracking in Edx:
Resilient object detection and tracking on Edge and cloud (AWS):
The best methods of object tracking run on GPU. The versioning of different
deep learning frameworks are crucial. For example the latest version of OS for
Jetson Nano
[Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN-
Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we
need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 can process up to 15 FPS with Full HD
videos. Methods on tracking is very different. First generation, it is
completely based on computer vision. The second generation combining Kalman
filter and advanced computer vision (SIFT), the third generation using deep
learning and some of the methods of previous generation like Kalman filter.
The fourth generation using combination of two deep learning methods. And the
latest generation using complete end to end models like RNN. Object tracking
works with all combination of environments such as, moving objects, moving
objects and camera in dynamic environments. As long as object appear in the
frame until disappeared it the tracking can track and identification as one
objects. No mater how many FPS.
#
Tracking
* Classic object tracking
* * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection.
* Kalman filtering, sparse and dense optical flow,
* Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter
* SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results.
* The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis.
* Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences.
* Multi-object tracking datasets
* large-scale benchmark Multi-Class Multi-object tracking datasets
* VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking.
* the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue.
* Temporal coherence. A feasible way to exploit temporal coherence is using object trackers
* Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance.
* ###
List of Datasets
* **MOT20**
* KITTI Tracking
* MOTChallenge 2015
* UA-DETRAC Tracking
* DukeMTMC
* Campus
* MOT17
* UAVDT-MOT
* VisDrone
###
Source code
* ROLO
* TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX)
* SiamMask
* PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn)
* Deep SORT
* PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
* TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe)
* TrackR-CNN
* TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5)
* Tracktor++
* PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8)
* JDE
* PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO)
* [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn)
#
Self collected datasets
##
[Video
labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome-
data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc)
* [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy)
* [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r)
* [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64)
#
Reference
1. Vision Meets Drones: Past, Present and Future
2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
3.
4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R)
5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv)
6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8)
7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC)
8.
some examples
Endeavor to summarize MOT:
The best methods running on GPU. The versioning of different deep learning
frameworks are crucial. For example the latest version of OS for Jetson Nano
"Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need
to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 may run in real time.
Methods on tracking is very different. First generation, it is completely
based on computer vision. The second generation combining machine learning,
Kalman filter and advanced computer vision (SIFT), the third generation using
deep learning and some of the methods of previous generation like Kalman
filter. The fourth generation using combination of two deep learning methods.
And the latest generation using complete end to end models with RNN.
Object tracking works with all combination of environments such as, moving
objects, moving objects and camera in dynamic environments. As long as object
appear in the frame until disappeared it the tracking can track and
identification as one objects. No mater how many FPS.
In around 130 videos of the course of Multiple Object Tracking on EDEX means
this topic is huge and require more attention for the more research and
development.
Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is
arm based and not many package are build for it.
Datasets for Tracking:
MOTChallenge
MOT15
MOT16/17
MOT19
KITTI
UA-DETRAC tracking benchmark
_metrics_
* _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames.
* _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment.
* _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames.
_False trajectories_ : predicted trajectories which do not correspond to a
real object (i.e. to a ground truth trajectory).
* _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is
mistakenly changed.
Test:
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Only Ubuntu, Not mac, can based on GPU, webcam not working
[https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp)
Only GPU
YouTube:
OpenCV
[Tracking Objects | OpenCV Python Tutorials for Beginners
2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop-
RoboticsandAI)
Multiple Object Tracking
[Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and
Deep SORT [FULL
COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy)
There are at least 7 types of tracker algorithms that can be[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[
](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC):
not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
* MIL
* BOOSTING
* MEDIANFLOW
* TLD
* KCF
* GOTURN
* MOSSE
Kalman filtering, sparse and dense optical flow are[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK)[Simple Online and Realtime Tracking
(SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK), which uses a combination of the Hungarian algorithm and Kalman filter to
achieve decent object tracking.
R-CNN
around 2000 region proposals
[selective
search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7)
share colors and textures, lightning conditions
slow to train and test
Fast R-CNN
computes a convolutional feature map for the entire input image in a single
forward pass of the network
architecture is trained end-to-end with a multi-task loss
[https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
Simple Online and Realtime Tracking with a Deep Association Metric. 2017
[https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp)
[https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg)
#
[ **The online course about multiple object tracking in
Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti-
object-tracking-for-automotive-
systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql)
Course Section 0: Welcome and Introduction '
Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition
and definitions: 15 videos
[Introductory
examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk)
Is about the accurate perception of the driving environment
Avoid collisions at the airport
Crowd surveillance
Crowd behavior
Planning of emergency procedures
Pedestrian tracking using LIDAR
Tracking based on detections
Group behavior
Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23
videos
[Introduction to SOT in
Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc-
SidNL1kRF7)
Pruning and merging
Pruning : remove hypotheses with small weights (and renormalize)
Merging: approximate a mixture of densities by a single density (often
Gaussian)
Gating: technique to disregard unreasonable detections [pruning]
SOT
* Gaussian densities
* Nearest neighbour (NN) filter [pruning]
* Probabilistic data association (PDA) filter [merging]
* Gaussian mixture densites
* Gaussian sum filter (GSF) [pruning/merging]
Part 3: Tracking a known number of objects in clutter 30
3.3.6 Predicting the n object density
**3.4.1 Introduction to data association**
Part 4: Random Finite Sets 24
Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube]
Part 6: Outlook - what is next? 18 [only in YouTube]
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# Video Tracking
Tracking
List of Datasets
Source code
Self collected datasets
Video labeling
Reference
The online course about multiple object tracking in Edx:
Resilient object detection and tracking on Edge and cloud (AWS):
The best methods of object tracking run on GPU. The versioning of different
deep learning frameworks are crucial. For example the latest version of OS for
Jetson Nano
[Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN-
Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we
need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 can process up to 15 FPS with Full HD
videos. Methods on tracking is very different. First generation, it is
completely based on computer vision. The second generation combining Kalman
filter and advanced computer vision (SIFT), the third generation using deep
learning and some of the methods of previous generation like Kalman filter.
The fourth generation using combination of two deep learning methods. And the
latest generation using complete end to end models like RNN. Object tracking
works with all combination of environments such as, moving objects, moving
objects and camera in dynamic environments. As long as object appear in the
frame until disappeared it the tracking can track and identification as one
objects. No mater how many FPS.
#
Tracking
* Classic object tracking
* * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection.
* Kalman filtering, sparse and dense optical flow,
* Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter
* SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results.
* The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis.
* Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences.
* Multi-object tracking datasets
* large-scale benchmark Multi-Class Multi-object tracking datasets
* VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking.
* the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue.
* Temporal coherence. A feasible way to exploit temporal coherence is using object trackers
* Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance.
* ###
List of Datasets
* **MOT20**
* KITTI Tracking
* MOTChallenge 2015
* UA-DETRAC Tracking
* DukeMTMC
* Campus
* MOT17
* UAVDT-MOT
* VisDrone
###
Source code
* ROLO
* TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX)
* SiamMask
* PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn)
* Deep SORT
* PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
* TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe)
* TrackR-CNN
* TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5)
* Tracktor++
* PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8)
* JDE
* PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO)
* [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn)
#
Self collected datasets
##
[Video
labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome-
data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc)
* [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy)
* [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r)
* [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64)
#
Reference
1. Vision Meets Drones: Past, Present and Future
2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
3.
4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R)
5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv)
6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8)
7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC)
8.
some examples
Endeavor to summarize MOT:
The best methods running on GPU. The versioning of different deep learning
frameworks are crucial. For example the latest version of OS for Jetson Nano
"Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need
to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 may run in real time.
Methods on tracking is very different. First generation, it is completely
based on computer vision. The second generation combining machine learning,
Kalman filter and advanced computer vision (SIFT), the third generation using
deep learning and some of the methods of previous generation like Kalman
filter. The fourth generation using combination of two deep learning methods.
And the latest generation using complete end to end models with RNN.
Object tracking works with all combination of environments such as, moving
objects, moving objects and camera in dynamic environments. As long as object
appear in the frame until disappeared it the tracking can track and
identification as one objects. No mater how many FPS.
In around 130 videos of the course of Multiple Object Tracking on EDEX means
this topic is huge and require more attention for the more research and
development.
Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is
arm based and not many package are build for it.
Datasets for Tracking:
MOTChallenge
MOT15
MOT16/17
MOT19
KITTI
UA-DETRAC tracking benchmark
_metrics_
* _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames.
* _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment.
* _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames.
_False trajectories_ : predicted trajectories which do not correspond to a
real object (i.e. to a ground truth trajectory).
* _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is
mistakenly changed.
Test:
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Only Ubuntu, Not mac, can based on GPU, webcam not working
[https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp)
Only GPU
YouTube:
OpenCV
[Tracking Objects | OpenCV Python Tutorials for Beginners
2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop-
RoboticsandAI)
Multiple Object Tracking
[Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and
Deep SORT [FULL
COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy)
There are at least 7 types of tracker algorithms that can be[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[
](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC):
not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
* MIL
* BOOSTING
* MEDIANFLOW
* TLD
* KCF
* GOTURN
* MOSSE
Kalman filtering, sparse and dense optical flow are[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK)[Simple Online and Realtime Tracking
(SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK), which uses a combination of the Hungarian algorithm and Kalman filter to
achieve decent object tracking.
R-CNN
around 2000 region proposals
[selective
search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7)
share colors and textures, lightning conditions
slow to train and test
Fast R-CNN
computes a convolutional feature map for the entire input image in a single
forward pass of the network
architecture is trained end-to-end with a multi-task loss
[https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
Simple Online and Realtime Tracking with a Deep Association Metric. 2017
[https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp)
[https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg)
#
[ **The online course about multiple object tracking in
Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti-
object-tracking-for-automotive-
systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql)
Course Section 0: Welcome and Introduction '
Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition
and definitions: 15 videos
[Introductory
examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk)
Is about the accurate perception of the driving environment
Avoid collisions at the airport
Crowd surveillance
Crowd behavior
Planning of emergency procedures
Pedestrian tracking using LIDAR
Tracking based on detections
Group behavior
Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23
videos
[Introduction to SOT in
Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc-
SidNL1kRF7)
Pruning and merging
Pruning : remove hypotheses with small weights (and renormalize)
Merging: approximate a mixture of densities by a single density (often
Gaussian)
Gating: technique to disregard unreasonable detections [pruning]
SOT
* Gaussian densities
* Nearest neighbour (NN) filter [pruning]
* Probabilistic data association (PDA) filter [merging]
* Gaussian mixture densites
* Gaussian sum filter (GSF) [pruning/merging]
Part 3: Tracking a known number of objects in clutter 30
3.3.6 Predicting the n object density
**3.4.1 Introduction to data association**
Part 4: Random Finite Sets 24
Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube]
Part 6: Outlook - what is next? 18 [only in YouTube]
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# Video Tracking
Tracking
List of Datasets
Source code
Self collected datasets
Video labeling
Reference
The online course about multiple object tracking in Edx:
Resilient object detection and tracking on Edge and cloud (AWS):
The best methods of object tracking run on GPU. The versioning of different
deep learning frameworks are crucial. For example the latest version of OS for
Jetson Nano
[Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN-
Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we
need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 can process up to 15 FPS with Full HD
videos. Methods on tracking is very different. First generation, it is
completely based on computer vision. The second generation combining Kalman
filter and advanced computer vision (SIFT), the third generation using deep
learning and some of the methods of previous generation like Kalman filter.
The fourth generation using combination of two deep learning methods. And the
latest generation using complete end to end models like RNN. Object tracking
works with all combination of environments such as, moving objects, moving
objects and camera in dynamic environments. As long as object appear in the
frame until disappeared it the tracking can track and identification as one
objects. No mater how many FPS.
#
Tracking
* Classic object tracking
* * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection.
* Kalman filtering, sparse and dense optical flow,
* Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter
* SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results.
* The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis.
* Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences.
* Multi-object tracking datasets
* large-scale benchmark Multi-Class Multi-object tracking datasets
* VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking.
* the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue.
* Temporal coherence. A feasible way to exploit temporal coherence is using object trackers
* Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance.
* ###
List of Datasets
* **MOT20**
* KITTI Tracking
* MOTChallenge 2015
* UA-DETRAC Tracking
* DukeMTMC
* Campus
* MOT17
* UAVDT-MOT
* VisDrone
###
Source code
* ROLO
* TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX)
* SiamMask
* PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn)
* Deep SORT
* PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
* TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe)
* TrackR-CNN
* TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5)
* Tracktor++
* PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8)
* JDE
* PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO)
* [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn)
#
Self collected datasets
##
[Video
labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome-
data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc)
* [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy)
* [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r)
* [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64)
#
Reference
1. Vision Meets Drones: Past, Present and Future
2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
3.
4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R)
5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv)
6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8)
7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC)
8.
some examples
Endeavor to summarize MOT:
The best methods running on GPU. The versioning of different deep learning
frameworks are crucial. For example the latest version of OS for Jetson Nano
"Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need
to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 may run in real time.
Methods on tracking is very different. First generation, it is completely
based on computer vision. The second generation combining machine learning,
Kalman filter and advanced computer vision (SIFT), the third generation using
deep learning and some of the methods of previous generation like Kalman
filter. The fourth generation using combination of two deep learning methods.
And the latest generation using complete end to end models with RNN.
Object tracking works with all combination of environments such as, moving
objects, moving objects and camera in dynamic environments. As long as object
appear in the frame until disappeared it the tracking can track and
identification as one objects. No mater how many FPS.
In around 130 videos of the course of Multiple Object Tracking on EDEX means
this topic is huge and require more attention for the more research and
development.
Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is
arm based and not many package are build for it.
Datasets for Tracking:
MOTChallenge
MOT15
MOT16/17
MOT19
KITTI
UA-DETRAC tracking benchmark
_metrics_
* _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames.
* _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment.
* _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames.
_False trajectories_ : predicted trajectories which do not correspond to a
real object (i.e. to a ground truth trajectory).
* _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is
mistakenly changed.
Test:
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Only Ubuntu, Not mac, can based on GPU, webcam not working
[https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp)
Only GPU
YouTube:
OpenCV
[Tracking Objects | OpenCV Python Tutorials for Beginners
2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop-
RoboticsandAI)
Multiple Object Tracking
[Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and
Deep SORT [FULL
COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy)
There are at least 7 types of tracker algorithms that can be[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[
](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC):
not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
* MIL
* BOOSTING
* MEDIANFLOW
* TLD
* KCF
* GOTURN
* MOSSE
Kalman filtering, sparse and dense optical flow are[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK)[Simple Online and Realtime Tracking
(SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK), which uses a combination of the Hungarian algorithm and Kalman filter to
achieve decent object tracking.
R-CNN
around 2000 region proposals
[selective
search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7)
share colors and textures, lightning conditions
slow to train and test
Fast R-CNN
computes a convolutional feature map for the entire input image in a single
forward pass of the network
architecture is trained end-to-end with a multi-task loss
[https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
Simple Online and Realtime Tracking with a Deep Association Metric. 2017
[https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp)
[https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg)
#
[ **The online course about multiple object tracking in
Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti-
object-tracking-for-automotive-
systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql)
Course Section 0: Welcome and Introduction '
Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition
and definitions: 15 videos
[Introductory
examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk)
Is about the accurate perception of the driving environment
Avoid collisions at the airport
Crowd surveillance
Crowd behavior
Planning of emergency procedures
Pedestrian tracking using LIDAR
Tracking based on detections
Group behavior
Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23
videos
[Introduction to SOT in
Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc-
SidNL1kRF7)
Pruning and merging
Pruning : remove hypotheses with small weights (and renormalize)
Merging: approximate a mixture of densities by a single density (often
Gaussian)
Gating: technique to disregard unreasonable detections [pruning]
SOT
* Gaussian densities
* Nearest neighbour (NN) filter [pruning]
* Probabilistic data association (PDA) filter [merging]
* Gaussian mixture densites
* Gaussian sum filter (GSF) [pruning/merging]
Part 3: Tracking a known number of objects in clutter 30
3.3.6 Predicting the n object density
**3.4.1 Introduction to data association**
Part 4: Random Finite Sets 24
Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube]
Part 6: Outlook - what is next? 18 [only in YouTube]
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# Video Tracking
Tracking
List of Datasets
Source code
Self collected datasets
Video labeling
Reference
The online course about multiple object tracking in Edx:
Resilient object detection and tracking on Edge and cloud (AWS):
The best methods of object tracking run on GPU. The versioning of different
deep learning frameworks are crucial. For example the latest version of OS for
Jetson Nano
[Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN-
Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we
need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 can process up to 15 FPS with Full HD
videos. Methods on tracking is very different. First generation, it is
completely based on computer vision. The second generation combining Kalman
filter and advanced computer vision (SIFT), the third generation using deep
learning and some of the methods of previous generation like Kalman filter.
The fourth generation using combination of two deep learning methods. And the
latest generation using complete end to end models like RNN. Object tracking
works with all combination of environments such as, moving objects, moving
objects and camera in dynamic environments. As long as object appear in the
frame until disappeared it the tracking can track and identification as one
objects. No mater how many FPS.
#
Tracking
* Classic object tracking
* * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection.
* Kalman filtering, sparse and dense optical flow,
* Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter
* SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results.
* The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis.
* Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences.
* Multi-object tracking datasets
* large-scale benchmark Multi-Class Multi-object tracking datasets
* VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking.
* the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue.
* Temporal coherence. A feasible way to exploit temporal coherence is using object trackers
* Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance.
* ###
List of Datasets
* **MOT20**
* KITTI Tracking
* MOTChallenge 2015
* UA-DETRAC Tracking
* DukeMTMC
* Campus
* MOT17
* UAVDT-MOT
* VisDrone
###
Source code
* ROLO
* TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX)
* SiamMask
* PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn)
* Deep SORT
* PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
* TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe)
* TrackR-CNN
* TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5)
* Tracktor++
* PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8)
* JDE
* PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO)
* [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn)
#
Self collected datasets
##
[Video
labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome-
data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc)
* [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy)
* [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r)
* [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64)
#
Reference
1. Vision Meets Drones: Past, Present and Future
2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
3.
4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R)
5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv)
6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8)
7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC)
8.
some examples
Endeavor to summarize MOT:
The best methods running on GPU. The versioning of different deep learning
frameworks are crucial. For example the latest version of OS for Jetson Nano
"Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need
to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 may run in real time.
Methods on tracking is very different. First generation, it is completely
based on computer vision. The second generation combining machine learning,
Kalman filter and advanced computer vision (SIFT), the third generation using
deep learning and some of the methods of previous generation like Kalman
filter. The fourth generation using combination of two deep learning methods.
And the latest generation using complete end to end models with RNN.
Object tracking works with all combination of environments such as, moving
objects, moving objects and camera in dynamic environments. As long as object
appear in the frame until disappeared it the tracking can track and
identification as one objects. No mater how many FPS.
In around 130 videos of the course of Multiple Object Tracking on EDEX means
this topic is huge and require more attention for the more research and
development.
Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is
arm based and not many package are build for it.
Datasets for Tracking:
MOTChallenge
MOT15
MOT16/17
MOT19
KITTI
UA-DETRAC tracking benchmark
_metrics_
* _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames.
* _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment.
* _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames.
_False trajectories_ : predicted trajectories which do not correspond to a
real object (i.e. to a ground truth trajectory).
* _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is
mistakenly changed.
Test:
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Only Ubuntu, Not mac, can based on GPU, webcam not working
[https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp)
Only GPU
YouTube:
OpenCV
[Tracking Objects | OpenCV Python Tutorials for Beginners
2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop-
RoboticsandAI)
Multiple Object Tracking
[Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and
Deep SORT [FULL
COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy)
There are at least 7 types of tracker algorithms that can be[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[
](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC):
not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
* MIL
* BOOSTING
* MEDIANFLOW
* TLD
* KCF
* GOTURN
* MOSSE
Kalman filtering, sparse and dense optical flow are[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK)[Simple Online and Realtime Tracking
(SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK), which uses a combination of the Hungarian algorithm and Kalman filter to
achieve decent object tracking.
R-CNN
around 2000 region proposals
[selective
search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7)
share colors and textures, lightning conditions
slow to train and test
Fast R-CNN
computes a convolutional feature map for the entire input image in a single
forward pass of the network
architecture is trained end-to-end with a multi-task loss
[https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
Simple Online and Realtime Tracking with a Deep Association Metric. 2017
[https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp)
[https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg)
#
[ **The online course about multiple object tracking in
Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti-
object-tracking-for-automotive-
systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql)
Course Section 0: Welcome and Introduction '
Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition
and definitions: 15 videos
[Introductory
examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk)
Is about the accurate perception of the driving environment
Avoid collisions at the airport
Crowd surveillance
Crowd behavior
Planning of emergency procedures
Pedestrian tracking using LIDAR
Tracking based on detections
Group behavior
Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23
videos
[Introduction to SOT in
Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc-
SidNL1kRF7)
Pruning and merging
Pruning : remove hypotheses with small weights (and renormalize)
Merging: approximate a mixture of densities by a single density (often
Gaussian)
Gating: technique to disregard unreasonable detections [pruning]
SOT
* Gaussian densities
* Nearest neighbour (NN) filter [pruning]
* Probabilistic data association (PDA) filter [merging]
* Gaussian mixture densites
* Gaussian sum filter (GSF) [pruning/merging]
Part 3: Tracking a known number of objects in clutter 30
3.3.6 Predicting the n object density
**3.4.1 Introduction to data association**
Part 4: Random Finite Sets 24
Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube]
Part 6: Outlook - what is next? 18 [only in YouTube]
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# Video Tracking
Tracking
List of Datasets
Source code
Self collected datasets
Video labeling
Reference
The online course about multiple object tracking in Edx:
Resilient object detection and tracking on Edge and cloud (AWS):
The best methods of object tracking run on GPU. The versioning of different
deep learning frameworks are crucial. For example the latest version of OS for
Jetson Nano
[Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN-
Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we
need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 can process up to 15 FPS with Full HD
videos. Methods on tracking is very different. First generation, it is
completely based on computer vision. The second generation combining Kalman
filter and advanced computer vision (SIFT), the third generation using deep
learning and some of the methods of previous generation like Kalman filter.
The fourth generation using combination of two deep learning methods. And the
latest generation using complete end to end models like RNN. Object tracking
works with all combination of environments such as, moving objects, moving
objects and camera in dynamic environments. As long as object appear in the
frame until disappeared it the tracking can track and identification as one
objects. No mater how many FPS.
#
Tracking
* Classic object tracking
* * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection.
* Kalman filtering, sparse and dense optical flow,
* Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter
* SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results.
* The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis.
* Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences.
* Multi-object tracking datasets
* large-scale benchmark Multi-Class Multi-object tracking datasets
* VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking.
* the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue.
* Temporal coherence. A feasible way to exploit temporal coherence is using object trackers
* Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance.
* ###
List of Datasets
* **MOT20**
* KITTI Tracking
* MOTChallenge 2015
* UA-DETRAC Tracking
* DukeMTMC
* Campus
* MOT17
* UAVDT-MOT
* VisDrone
###
Source code
* ROLO
* TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX)
* SiamMask
* PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn)
* Deep SORT
* PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
* TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe)
* TrackR-CNN
* TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5)
* Tracktor++
* PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8)
* JDE
* PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO)
* [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn)
#
Self collected datasets
##
[Video
labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome-
data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc)
* [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy)
* [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r)
* [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64)
#
Reference
1. Vision Meets Drones: Past, Present and Future
2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
3.
4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R)
5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv)
6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8)
7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC)
8.
some examples
Endeavor to summarize MOT:
The best methods running on GPU. The versioning of different deep learning
frameworks are crucial. For example the latest version of OS for Jetson Nano
"Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need
to install lower Jetpack version on Jetson Nano or compile the Pytorch. I
compile Pytorch and it takes few hours with a lot of issue to solve. For the
Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and
compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is
possible but takes time to solve conflicts also supporting eGPU is another
issue for lower ubuntu. On MacOS installing everything is easy because of not
supporting GPU but many library and frameworks of the source codes of tracking
require GPU version. Even install CPU version of all library does not grantee
to run tracking methods. Another aspect is speed. Running tracking even on GPU
is very slow based on my experience using Yolo version 3 which is one of the
fastest object detection on GTX 2070 may run in real time.
Methods on tracking is very different. First generation, it is completely
based on computer vision. The second generation combining machine learning,
Kalman filter and advanced computer vision (SIFT), the third generation using
deep learning and some of the methods of previous generation like Kalman
filter. The fourth generation using combination of two deep learning methods.
And the latest generation using complete end to end models with RNN.
Object tracking works with all combination of environments such as, moving
objects, moving objects and camera in dynamic environments. As long as object
appear in the frame until disappeared it the tracking can track and
identification as one objects. No mater how many FPS.
In around 130 videos of the course of Multiple Object Tracking on EDEX means
this topic is huge and require more attention for the more research and
development.
Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is
arm based and not many package are build for it.
Datasets for Tracking:
MOTChallenge
MOT15
MOT16/17
MOT19
KITTI
UA-DETRAC tracking benchmark
_metrics_
* _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames.
* _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment.
* _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames.
_False trajectories_ : predicted trajectories which do not correspond to a
real object (i.e. to a ground truth trajectory).
* _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is
mistakenly changed.
Test:
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Only Ubuntu, Not mac, can based on GPU, webcam not working
[https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp)
Only GPU
YouTube:
OpenCV
[Tracking Objects | OpenCV Python Tutorials for Beginners
2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop-
RoboticsandAI)
Multiple Object Tracking
[Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and
Deep SORT [FULL
COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy)
There are at least 7 types of tracker algorithms that can be[
](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv-
vehicle-detection-tracking-and-speed-
estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[
](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC):
not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-
detection-and-tracking-
in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)
* MIL
* BOOSTING
* MEDIANFLOW
* TLD
* KCF
* GOTURN
* MOSSE
Kalman filtering, sparse and dense optical flow are[
](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK)[Simple Online and Realtime Tracking
(SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN-
xK), which uses a combination of the Hungarian algorithm and Kalman filter to
achieve decent object tracking.
R-CNN
around 2000 region proposals
[selective
search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7)
share colors and textures, lightning conditions
slow to train and test
Fast R-CNN
computes a convolutional feature map for the entire input image in a single
forward pass of the network
architecture is trained end-to-end with a multi-task loss
[https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH)
Simple Online and Realtime Tracking with a Deep Association Metric. 2017
[https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp)
[https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg)
#
[ **The online course about multiple object tracking in
Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti-
object-tracking-for-automotive-
systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql)
Course Section 0: Welcome and Introduction '
Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition
and definitions: 15 videos
[Introductory
examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk)
Is about the accurate perception of the driving environment
Avoid collisions at the airport
Crowd surveillance
Crowd behavior
Planning of emergency procedures
Pedestrian tracking using LIDAR
Tracking based on detections
Group behavior
Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23
videos
[Introduction to SOT in
Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc-
SidNL1kRF7)
Pruning and merging
Pruning : remove hypotheses with small weights (and renormalize)
Merging: approximate a mixture of densities by a single density (often
Gaussian)
Gating: technique to disregard unreasonable detections [pruning]
SOT
* Gaussian densities
* Nearest neighbour (NN) filter [pruning]
* Probabilistic data association (PDA) filter [merging]
* Gaussian mixture densites
* Gaussian sum filter (GSF) [pruning/merging]
Part 3: Tracking a known number of objects in clutter 30
3.3.6 Predicting the n object density
**3.4.1 Introduction to data association**
Part 4: Random Finite Sets 24
Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube]
Part 6: Outlook - what is next? 18 [only in YouTube]
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# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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[Computer Vision,
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# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
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Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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[Computer Vision,
Deep Learning, Artificial superintelligence (ASI)](/home)
# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
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Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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[Computer Vision,
Deep Learning, Artificial superintelligence (ASI)](/home)
# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
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Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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[Computer Vision,
Deep Learning, Artificial superintelligence (ASI)](/home)
# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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# Share
I would like to give you some of my experience with AI projects.
image processing tips:
Preparation ML Project Workflow
Before Training deep learning model
Training deep learning model
Continuous delivery
After Training deep learning model
Deep learning model in production
Technology
My Keynote (February 2021)
Advanced and practical
Face
I am thrilled to announce the launch of my new service! As a computer vision
and machine learning consultant, I provide end-to-end research and development
solutions for cutting-edge artificial intelligence projects. My services
encompass custom software implementation, MLOps, and project management,
ensuring clients receive top-quality results. If you're looking to enhance
your AI capabilities, I'm here to help. Contact me to learn more.
Are you looking for expert analysis of your project, eager for professional
feedback, or in need of a comprehensive execution plan? Would you like to gain
insights from industry leaders? I offer a 15-minute consultation free of
charge to help you achieve your goals.
improved performance, reduced costs, or increased customer satisfaction.
Are you in search of a partner who aligns with your project requirements? As a
freelance data analyst with over 10 years of experience in the industry, I
understand that finding the right partner can be challenging.
To help businesses overcome this hurdle, I offer best-in-class analysis tools
such as regression analysis, reliability tools, hypothesis tests, and a graph
builder feature for scientific data visualization. Additionally, I use
predictive modeling to forecast future market conditions, making it easier for
businesses to plan and strategize.
I understand that budget limitations can exist, and gaining buy-in for new
tools and systems can be burdensome. That's why my services are designed to be
user-friendly, with no coding required and built-in content-sensitive help
systems. My tools and systems are also designed for non-statisticians, making
it easier for businesses to understand and implement them.
With many industry use cases available online, my services are proven to
deliver results. Let's partner up to take your project to the next level!
pip install mlc-ai-nightly -f https://mlc.ai/wheels
https://mlc.ai/
https://mlc.ai/summer22/
Day 1:
Introduction to Unity: TVMScript
Introduction to Unity: Relax and PyTorch
TVM BYOC in Practice
Get Started with TVM on Adreno GPU
Introduction to Unity: Metaschedule
How to Bring microTVM to a custom IDE
Day 2:
Community Keynote
PyTorch 2.0: the journey to bringing compiler technologies to the core of
PyTorch
Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for
Acceleration
On-Device Training Under 256KB Memory
AMD Tutorial
TVM at TI: Accelerating inference using the C7x/MMA
Adreno GPU: 4x speed-up and upstreaming to TVM mainline
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code
Generation
Improvement in the TVM OpenCL codegen to autogenerate optimal convolution
kernels for Adreno GPUs
TVM Unity: Pass Infrastructure and BYOC
Renesas Hardware accelerators with Apache TVM
Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs
Towards Building a Responsible Data Economy
Optimizing SYCL Device Kernels with AKG
Adreno GPU Performance Enhancements using TVM
Improvements to CMSIS-NN integration in TVM
UMA: Universal Modular Accelerator Interface
Day 3:
TVM Unity for Dynamic Models
Empower Tensorflow serving with backend TVM
Enabling Conditional Computing on Hexagon target
Decoupled Model Schedule for Large Deep Learning Model Training
Using TVM to bring Bayesian neural networks to embedded hardware
Efficient Support of TVM Scan OP on RISC-V Vector Extension
Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor
boards
Compiling Dynamic Shapes
TVM Packaging in 2023: delivering TVM to end users
Cross-Platform Training Using Automatic Differentiation on Relax IR
AutoTVM: Reducing tuning space by cross axis filtering
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Analytical Tensorization and Fusion for Compute-intensive Operators
CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra
Library
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Automating DL Compiler Bug Finding with NNSmith
TVM at NIO
TVM at Tencent
Integrating the Andes RISC-V Processors into TVM
Alpa: A Compiler for Distributed Deep Learning
ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of
Dynamic Deep Learning Computations
Channel Folding: a Transform Pass for Optimizing Mobilenets
========================================================================Day 1:
************************ Introduction to Unity: TVMScript
[https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM-
Demo/blob/main/tvmscript.ipynb)
Gan NN show us some hidden patter in history we can not see before.
“I always have a slip of paper at hand, on which I note down the ideas of
certain pages. On the backside I write down the bibliographic details. After
finishing the book I go through my notes and think how these notes might be
relevant for already written notes in the slip-box. It means that I always
read with an eye towards possible connections in the slip-box.” (Luhmann et
al., 1987, 150)
Deep representation learning
Model evaluation.
Camera cheaper lidar
Point cloud because of we need 3d
Capturing reality
1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥
Standard way: git add .
git commit -m "Message"
Another way: git commit -a -m "Message"
𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬
With aliases, you can write your own Git commands that do anything you want.
Eg: git config --global alias.ac '!git add -A && git commit -m'
(alias called ac, git add -A && git commit -m will do the full add and commit)
𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭
The revert command simply allows us to undo any commit on the current branch.
Eg: git revert 486bdb2
Another way: git revert HEAD (for recent commits)
𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠
This command lets you easily see the recent commits, pulls, resets, pushes,
etc on your local machine.
Eg: git reflog
𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬
Gives you the ability to print out a pretty log of your commits/branches.
Eg: git log --graph --decorate --oneline
𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬
One can also use the log command to search for specific changes in the code.
Eg: git log -S "A promise in JavaScript is very similar"
𝟕\. 𝐒𝐭𝐚𝐬𝐡
This command will stash (store them locally) all your code changes but does
not actually commit them.
Eg: git stash
𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬
This command will delete all the tracking information for branches that are on
your local machine that are not in the remote repository, but it does not
delete your local branches.
Eg: git remote update --prune
𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭
For finding which commits caused certain bugs
Eg: git bisect start
git bisect bad
git bisect good 48c86d6
𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬
One can wipe out all changes on your local branch to exactly what is in the
remote branch.
Eg: git reset --hard origin/main
Don’t trust your devices IoT. software and hardware are together for better
business.
Newsletter investing every 3 months
1\. Prototyping. New bie
2\. Patent. Website. ( list of investors)
3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000
preseed. Quveribel. Equtible rund convertible non agreement Template.
Convertabel lone
1\. Germ standar inistitude
2\.
4\. Equity. Venture builder. 20% 200 000
5\. 100 000 per year to become unocorn in less than 10 years
6\. Soniy corn 100k unicorn 1M
7\. 360 euro per years for database of investor
8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in
10M) convert on based .
9\. Invester Never act as co-founder = full time = 20%
10\. Project profit,
11\. Full time after foun rising
Make a plan for your business; take your time to make calculations by creating
a target audience. Your target audience determines how you approach your
business plan. By studying your target audience, you are making empirical
research and collecting information from them Then, secure a good partnership
if need be, and get enough capital to start up.
*
* What the people need
* Why people need it
* When the people need it
* It's affordability
* It's ease of use
* It's maintenance and revenue
Pair programming
The SB7 Framework harnesses the influence of stories. The structure describes
the 7 most common story elements:
• Character
• Problem
• Guide
• Plan
• Calls to action
• Failure
• Success
Dear [Hiring Manager’s Name],
I am writing to apply for the position of computer vision for IoT and cloud at
[Company Name]. I am a highly skilled and experienced computer vision engineer
with a strong background in IoT and cloud technologies. I believe that my
skills and experience make me an ideal candidate for this position and I am
excited about the opportunity to contribute to the success of your
organization.
I have a solid understanding of computer vision algorithms and techniques, as
well as experience in developing and implementing computer vision systems. I
am proficient in programming languages such as Python, C++, and Java, and have
experience with popular computer vision libraries such as OpenCV, TensorFlow,
and PyTorch.
In addition, I have a strong background in IoT and cloud technologies,
including experience with IoT platforms such as AWS IoT, Azure IoT, and Google
Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure,
and Google Cloud, and have experience with deploying and managing computer
vision systems on these platforms.
I am also a team player and have excellent communication skills. I am able to
work with cross-functional teams and can effectively communicate with both
technical and non-technical stakeholders. I am also highly motivated, and I am
always looking for ways to improve my skills and stay up-to-date with the
latest technologies.
I am excited about the opportunity to join [Company Name] and to contribute to
the development of cutting-edge computer vision systems for IoT and cloud. I
am confident that my skills and experience make me a strong candidate for this
position, and I look forward to discussing how I can contribute to your
organization.
Thank you for considering my application. I look forward to hearing from you
soon.
Sincerely,
Title: "Unlocking the Power of Computer Vision for IoT and Cloud"
Introduction:
* Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.
Body:
* First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models.
* One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner.
* Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly.
* Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.
Conclusion:
* Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.
Excited to share my latest project using computer vision and IoT to improve
efficiency in manufacturing. I used a combination of machine learning
algorithms and cloud computing to analyze data from cameras and sensors in
real-time, resulting in a 20% increase in production speed. This was a
challenging project but I enjoyed every step of it!
I am always looking for new opportunities to apply my skills in computer
vision and IoT to help companies improve their operations. Let's connect if
you are working on a similar project or if you are looking for a developer
with these skills. #computervision #IoT #cloudcomputing
#manufacturingefficiency #machinelearning #developer"
In this post, you briefly mention your experience and skills in computer
vision and IoT, and you provide a specific example of a project you worked on
that demonstrates your abilities. You also make it clear that you are open to
new opportunities, and you invite others to connect with you. Using relevant
hashtags such as #computervision #IoT #cloudcomputing can help your post reach
a wider audience
Exciting news! I just published a paper on a new object detection algorithm
that I developed. The algorithm uses a combination of deep learning and
computer vision techniques to improve accuracy and speed of object detection
in real-world scenarios. This is a big step forward in the field of computer
vision and I am proud to have contributed to it.
I will be presenting my research at the Computer Vision Conference next month,
if you're attending be sure to stop by and say hi! #computervision
#objectdetection #deeplearning #research"
In this post, you briefly explain the main findings and contributions of your
research, and you express your excitement and pride in your work. You also
mention the upcoming conference where you will be presenting your research,
inviting your friends and colleagues to meet you in person. Also using
relevant hashtags such as #computervision #objectdetection #deeplearning can
help reach a wider audience interested in the field.
Features stores
1\. Car parts detection
2\. Resize keep aspects ration
3\. 3.1 Perform damage detection
4\. 3.2Semantic segregation
5\. Transfer to original coordinates
1 class imbalance
2 class definition Maybe Class in between
3 inconstant annotations
Color augmentation
1\. RGB shift
2\. Random brithness and contrast
3\. Sharpen
4\. Hue saturation value
Why manually data augmented
Becasu control of data. Not too rotate or change something
Photogrammetry model
Neural radiance fields (NeRF)
NeRF in the wild
\
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
Yocto and Machine Learning + OpenCV:
[https://www.yoctoproject.org](https://www.yoctoproject.org)
[https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto-
image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning-
on-maaxboard-s-yocto-image-part-1-6a4796)
Bard Google: [https://blog.google/technology/ai/bard-google-ai-search-
updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/)
[https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git-
pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git-
pages)
Book: Project Management for Non-Project Managers
[https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1)
[https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی
[Accelerate deep learning model development with cloud custom environments -
AWS Online Tech Talks -
YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1)
[بخش هایی از کتاب Refactoring (نسخه
رایگان)](https://www.developit.ir/refactoring/free.html#f7)
[Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning
AI](https://lightning.ai/pages/community/community-discussions/performance-
notes-of-pytorch-support-for-m1-and-m2-gpus/)
[Investopedia Academy](https://academy.investopedia.com/)
[HandBrake updated with AV1 and VP9 10-bit video
encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and-
vp9-10-bit/)
[How to Start Your Sole Proprietorship in 6 Simple
Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship-
in-germany)
[Duolingo English Test](https://englishtest.duolingo.com/applicants)
[چالشهای تولید محتوا برای مارکت اروپا و آمریکا -
YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ)
[PyTorch for Deep Learning & Machine Learning – Full Course -
YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog)
[Why passive investing makes less sense in the current environment | Financial
Times](https://archive.ph/0VucZ)
[GitHub - google-research/tuning_playbook: A playbook for systematically
maximizing the performance of deep learning
models.](https://github.com/google-research/tuning_playbook)
[GitHub - mgechev/google-interview-preparation-problems: leetcode problems I
solved to prepare for my Google interview.](https://github.com/mgechev/google-
interview-preparation-problems)
[Bayesian Neural Networks and Variational
Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood)
[One machine learning question every day -
bnomial](https://today.bnomial.com/?ref=email)
Git remote add orgine
Asynchronous
Operation
Anomaly detection
Use experience. Personalizes.
Prediction manage society mobility
Personalization
Covenant
Platform.
OpenMMLab
Wordtune - AI-powered Writing Companion
tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt
3D object using triangular mesh need vertices
point cloud underlying surface of some 3D object, faster
Definition of Done
User Story complete
Code\Implementation complete
Code\Implementation Peer Reviews) approved
Unit tests complete (if required)
Testing Notes complete (if required)
User Story Acceptance criteria defined and verified
Backend: Python, Redis, Postgres, Celery
Frontend: React, Redux, TypeScript
DevOps: Terraform, Kubernetes, GitHub, Docker, AWS
Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker
ML: Selcond core, Kubeflow, …
[Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29)
,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic
range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone
reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) ,
[Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29),
[Color](https://en.wikipedia.org/wiki/Color),
[Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR
lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras),
[Vignetting](https://en.wikipedia.org/wiki/Vignetting),
[Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral
[chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration)
(LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color,
[Artifacts](https://en.wikipedia.org/wiki/Compression_artifact)
۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید.
۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید.
۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه
دارید. متن میان دو انگشت انتخاب خواهد شد.
۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن)
پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید)
۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت،
یک بار روی آن تپ کنید.
namely motion estimation, motion smoothing, and image warping. Motion
estimation algorithms often use a similarity transform to handle camera
translations, rotations, and zooming. The tricky part is getting these
algorithms to lock onto the background motion,
0\. video frames captured during fast motion are often blurry. Their
appearance can be improved either using deblurring techniques (Section 10.3)
or stealing sharper pixels from other frames with less motion or better focus
(Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test
some of these ideas.
1\. Background subtraction
2\. Motion estimation
3\. Motion smoothing
4\. Image warping. image warping can result in missing borders around the
image, which must be cropped, filled using information from other frames, or
hallucinated using inpainting techniques (Section 10.5.1).
Vision stabilization
There is much recent work on
Multi-view 3D reconstruction is a central research topic in computer vision
that is driven in many different directions
There are many available methods that can handle the noisy image completion
problem
In the case of surveillance using a fixed camera, there is no desired motion.
In the case of most robotic applications, horizontal and vertical motions are
desired, but rotation is not. In some cases of ground vehicles where the
terrain is known to have many incline changes, or with aerial vehicles
undergoing complicated maneuvers where the vehicle’s body is meant to be in
varying orientations, rotation might be desired as the robot is meant to be at
an angle at times.
In robotics applications, computational complexity is extremely important due
to the need for real-time operation. Also, it is likely that the center of
rotation will not lie in the center of the image frame because the camera is
rarely mounted at the robot’s center of mass.
This first assumption is made in many video stabilization algorithms, and is a
convenient way to seed the correct features with higher trust values. It is
not an unreasonable assumption to make. Depending on the application, there is
often a large portion of frames where local motion does not occur. In some
situations, such as monitoring of steady traffic, there is no guarantee that
local motion will not occur. This situation has not been tested, nor has our
algorithm been designed to handle it. The second assumption comes from a
combination of common sense, and the experience of many computer vision
researchers. It makes sense that an object in the scene which does not move
will be recognized more easily and more often. Being recognized consistently
and consecutively is considered stable. On the other hand, objects which have
local motion are less likely to be recognized as often. They might move
through shadows, change orientation, or even move completely out of the scene.
These possibilities all lead to a less stable class of features. It is likely
that, more often than not, there are more background features than foreground
features. Moving objects generally cover a small portion of the screen, which
usually yields fewer features. Although uncommon, we did not want to make the
assumption that this would occur in every frame. Certain scenes will consist
of a large portion of local motion, or an object will move very close to the
camera, consuming a much larger portion of the scene than the background. As
long as some background features are discovered in each frame, our
stabilization algorithm should succeed.
#
image processing tips:
* the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.
* the coordinate of the image start at top left of the image/display
* in order to change it to the normal coordinate you can use
* grid of points; two matrix to X , Y coordinate
* subtract half of W, H from X, Y in order to have normal coordinate system for our image
* now we have cartesian coordinate
*
* cartesian coordinate to polar coordinate
* تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد
* in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten().
* in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.
* imge_mask=np.ones_like(image_source)*255
* imge_mask=imge_mask.astype(np.uint8)
* imge_mask=imge_mask.flatten() ??? .ravel()
* .asarray
* np.logical_and( 1, 2)
* indexes=[index for index in range(len(array1)) if array1[index] == True]
* cv2.bitwise_not(yyy)
*
"olive" editor remove silence
Questions:
How to train model to add new classes?
How to add a new class to an existing classifier in deep learning?
Adding new Class to One Shot Learning trained model
Is it possible to train a neural network as new classes are given?
Merging all several models that detection system for all these tasks.
Answer 1:
There are several ways to add new classes to the trained model, which require
just training for the new classes.
* Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning))
* continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme))
* online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning))
* Transfer Learning Twice
* Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning))
Answer 2:
Online learning is a term used to refer to a model which takes a continual or
sequential stream of input data while training, in contrast to offline
learning (also called batch learning), where the model is pre-trained on a
static predefined dataset.
Continual learning (also called incremental, continuous, lifelong learning)
refers to a branch of ML working in an online learning context where models
are designed to learn new tasks while maintaining performance on historic
tasks. It can be applied to multiple problem paradigms (including Class-
incremental learning, where each new task presents new class labels for an
ever expanding super-classification problem).
Do I need to train my whole model again on all four classes or is there any
way I can just train my model on new class?
Naively re-training the model on the updated dataset is indeed a solution.
Continual learning seeks to address contexts where access to historic data
(i.e. the original 3 classes) is not possible, or when retraining on an
increasingly large dataset is impractical (for efficiency, space, privacy etc
concerns). Multiple such models using different underlying architectures have
been proposed, but almost all examples exclusively deal with image
classification problems.
Answer 3:
You could use transfer learning (i.e. use a pre-trained model, then change its
last layer to accommodate the new classes, and re-train this slightly modified
model, maybe with a lower learning rate) to achieve that, but transfer
learning does not necessarily attempt to retain any of the previously acquired
information (especially if you don't use very small learning rates, you keep
on training and you do not freeze the weights of the convolutional layers),
but only to speed up training or when your new dataset is not big enough, by
starting from a model that has already learned general features that are
supposedly similar to the features needed for your specific task. There is
also the related domain adaptation problem.
There are more suitable approaches to perform incremental class learning
(which is what you are asking for!), which directly address the [catastrophic
forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance,
you can take a look at this paper [Class-incremental Learning via Deep Model
Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep
Model Consolidation (DMC) approach. There are other continual/incremental
learning approaches, many of them are described
[here](https://ai.stackexchange.com/a/24529/2444) or in more detail
[here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231).
Answer 4:
by using Continual learning approaches to trained without losing the original
classes. It has 3 categories:
Regularization
Expansion
Rehearsal
Answer 5:
if you access to the dataset then you can download it and add all you new
classes when you have " 'N' COCO Classes + 'M' New classes "
after that you can fine tune model based on new dataset. you do not need all
of the dataset just same number of image for all class enough.
[https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee-
mris/)
Before start your machine learning project ask these questions and
preparation: What is your inference hardware? specify the use case. specify
model interface. how would we monitor performance after deployment? how can we
approximate post-deployment monitoring before deployment? build a model and
iteratively improve it. How to deploy the model at the end? monitor
performance after deployment. what is your metric? How do you split your data
(training and validation)?
###
Preparation ML Project Workflow
* [What is your hardware ?](/topics-and-projects/hardware)
* specify the use case
* specify model interface
* how would we monitor performance after deployment?
* how can we approximate post-deployment monitoring before deployment?
* build a model and iteratively improve it
* deploy the model
* monitor performance
* what is your are metric?
* How do you split your data?
###
Before Training deep learning model
* using large model to train because
* it is faster to train with lower overfit and faster converge due to best training
* it is easier and higher compress in the final stage
* model compression and acceleration: reducing parameters without significantly decreasing the model performance
* Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case
* * The data you don't need: removing redundant samples
* get more data
* Invent more data
* data augmentation
* Re-scale data
* balance datasets
* Transform your data
* Feature selection based on dataset and use case
* ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.
###
Training deep learning model
* automated hyper-parameters
* Using Hyperparameter tuning / Hyperparameter optimization tools
* AutoML
* genetic algorithm
* population based training
* bayesian optimization
* You need to set some parameters and config for training
* * Diagnostics
* Weight Initialization
* Learning rate
* Activation function
* Network Topology
* Batches and Epochs
* Regularization
* Optimization and Loss
* Early Stopping
###
Continuous delivery
* evolve with latest detection models
* more data (no labels)
* semi-supervised learning: big self-supervised models are strong semi-supervised learners
###
After Training deep learning model
* Parameter pruning
* model pruning: reducing redundant parameters which are not sensitive to the performance.
* aim: remove all connections with absolute weights below a threshold
* Quantization
* compresses by reducing the number of bits used to represent the weights
* quantization effectively constraints the number of different weights we can use inside our kernels
* per-channel quantization for weights, which improves performance by model compression and latency reduction.
* Low rank matrix factorization (LRMF)
* there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data
* LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness
* Compact convolutional filters (Video/CNN)
* designing special structural convolutional filters to save parameters
* replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy
* Knowledge distillation
* training a compact neural network with distilled knowledge of a large model
* distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy
* Binarized Neural Networks (BNNs)
* Apache TVM (incubating) is a compiler stack for deep learning systems
* Neural Networks Compression Framework (NNCF)
###
Deep learning model in production
* security: controls access to model(s) through secure packaging and execution
* Test
* auto training
* using parallel processing and library such as GStreamer
#
Technology
Docker
AWS
Flask
Django
#
My Keynote (February 2021)
1. introduction
2. Machine Learning/ Deep Learning
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed
3. supervised Machine Learning
1. Deep Convolutional Neural Networks (DCNN) Architecture
2. Visualizing and Understanding Convolutional Networks
3. Object Detection by Deep Learning
4. [Video Tracking](/topics-and-projects/video-tracking)
5. Style Transfer
4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL)
1. Google
2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl)
5. unsupervised Machine Learning
1. Auto Encoder
6. Generative Adversarial Networks (GANs)
7. Tools
8. Pre trained model
9. Effect of Augmented Datasets to Train DCNNs
10. Training for more classes
11. Optimization
12. [Hardware](/topics-and-projects/hardware)
13. Production setup
14. post development
15. business , Gartner, Hype Cycle for emerging technologies, 2025
###
Advanced and practical
1. Inside CNN
1. Deep Convolutional Neural Networks Architecture
2. Convolution
3. Convolution Layer
4. Conv/FC Filters
5. Activation Functions
6. Layer Activations
7. Pooling Layer
8. Dropout ; L2 pooling
9. Why
1. Max-pooling is useful
2. How to see inside each layer and find important features
* Visualizing and Understanding Convolutional Networks
* [https://tensorspace.org/](https://tensorspace.org/)
* [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)
2. Hands on python for deep learning
3. Fundamental deep learning
4. Installation: TensorFlow, PyTorch
5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile)
Summary of the summit
* AI Hardware Europe Summit (July 2020)
* [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit)
* Apache TVM And Deep Learning Compilation Conference (December 2020)
* [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v)
[https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp
#
Face
* Effective and precise face detection based on color and depth data
* [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X)
* containing or not containing a face
* Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on.
* Viola–Jones detector
* illumination changes and occlusion
* depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector
* \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;
* \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;
* \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.
* The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach.
* image below
* Gaussian mixture 3D morphable face model
* [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527)
*
*
* Face Synthesis for Eyeglass-Robust Face Recognition
* [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face)
* GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
* [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and)
* FacePoseNet: Making a Case for Landmark-Free Face Alignment
* [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free)
* Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
* [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and)
* Unsupervised Eyeglasses Removal in the Wild
* [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild)
* How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
* [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf)
* (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets.
* (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images).
* (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W.
* (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.
* (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used.
* Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/)
19.Sep.2021
[Medium](https://medium.com/p/626019137fa9/edit)
[https://fi.co/madlibs](https://fi.co/madlibs)
[https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389)
Dreyer's English (learn write English)
#book story
Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth
**Papers:**
CALTag: High Precision Fiducial Markers for Camera
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study
:implementation variation of the laplacian
Analysis of focus measure operators in shape-from-focus: why laplacian? Blure
detection? Iqaf?
Optical flow modeling and computation: A survey
Toward general type 2 fuzzy logic systems based on zSlices
\--------------------------------------------------------------------
Lost in space
The OA
Film:[
https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank)
Movie Serial billons
monk serial movies
Python async
Highly decoupled microservice
Edex RIS-V , Self-car
RISC-V Magazine
Road map
Game: over/under
[https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed)
\--------------------------------------------------------------------
\--------------------------------------------------------------------
GDPR in IoT
The EU General Data Protection
Regulation (GDPR) and Face Images in IoT
The GDPR (General Data Protection Regulation), taking effect in May 2018,
introduces strict requirements for personal data protection and the privacy
rights of individuals. The EU regulations will set a new global standard for
privacy rights and change the way organizations worldwide store and process
personal data. The GDPR brings the importance of preserving the privacy of
personal information to the forefront, yet the importance of face images
within this context is often overlooked. The purpose of this paper is to
introduce a solution that helps companies protect face images in IoT devices
which record or process image by camera, to strengthen compliance with the
GDPR.
Our Face is our Identity
Our face is the most fundamental and highly visible element of our identity.
People recognize us when they see our face or a photo of our face.
Recent years have seen exponential increase in the use, storage and
dissemination of face images in both private and public sectors - in social
networks, corporate databases, IoT, smart-city deployments, digital media,
government applications, and nearly every organization’s databases.
\---------------------
$(aws-okta env stage)
aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip
aws s3 ls images | tail -n 100
aws s3 cp staging-images/test.jpg /Users/test.jpg
\---------------------
screen -rD
k get pods
Docker
RUN chmod +x /tmp/run.sh
Can run docker in terminal and run code line by line
docker run -it --rm debian:stable-slim bash
apt-get update
apt-get installl -y
\--------------------------------
brew install awscli aws-okta kubectx kubernetes-cli tfenv
touch ~/.aws/config
\--------------------------------------------------------------------
docker image rm TETSTDFSAFDSADF
docker image ls
docker system prune
docker run -p 5000:5000 nameDocker:latest
docker build . -t nameDocker:latest
docker container stop number-docker-name
docker container ls
* docker pull quay.io/test:v0.0.1
* docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1
* curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict)
* docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test
\--------------------------------
Cloud software engineer and consultant focusing on building highly available,
scalable and fully automated infrastructure environments on top of Amazon Web
Services and Microsoft Azure clouds. My goal is always to make my customers
happy in the cloud.
\----------------
Search google for 3d = tiger - iPhone show AR/VR
\---------------
brew install youtube-dl
\----------------------------
List: Collection bucket : 1 for week 2 for month 3 for future
\--------------------------------------------------------------------
**• Per frame operation**
– Detection
– Classification
– Segmentation
– Feature extraction
– Recognition
**• Across frames **
– Tracking
– Counting
**• High level**
– Intention
– Relations
– Analyzing
=============================
Deep compression
Pruning deep learning
Hash table neural network
Dl compression
Deep compression
===================================
Mini PCI-e slot
* What have I learned so far:
* Problem-based learning
* real life scenarios
* index card (answer , idea)
* Think-Pair-Share
* Leverage flip charts
* Summarizing
\--------------------------------------------------------------------
Self
\\\
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
\cite{Chen2020}
%https://github.com/google-research/simclr
first pretraining on a large unlabeled dataset and then fine-tuning on a
smaller labeled dataset
pretraining on large unlabeled image datasets, as demonstrated by Exemplar-
CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others.
“A Simple Framework for Contrastive Learning of Visual Representations”,
85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset
contrastive learning algorithms
linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et
al., 2019; Kolesnikov et al., 2019)
unsupervised learning benefits more from bigger models than its supervised
counterpart.
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
\--------------------------------------------------------------------
Some of optimization algorithms
========================
Swarm Algorithm
===============
1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior
of ant colonies
2\. Firefly Algorithm based on insects called fireflies
3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired
by the process of reproduction of Honey Bee
4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the
Honey Bees
5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps
6\. Bee Collecting Pollen Algorithm (BCPA)
7\. Termite Algorithm
8\. Mosquito swarms Algorithm (MSA)
9\. zooplankton swarms Algorithm (ZSA)
10\. Bumblebees Swarms Algorithm (BSA)
11\. Fish Swarm Algorithm (FSA)
12\. Bacteria Foraging Algorithm (BFA)
13\. Particle Swarm Optimization (PSO)
14\. Cuckoo Search
15\. Bat Algorithm (BA)
16\. Accelerated PSO
17\. Bee System
18\. Beehive Algorithm
19\. Cat Swarm
20\. Consultant-guided search
21\. Eagle Strategy
22\. Fast Backterial swarming algorithm
23\. Good lattice swarm optimization
24\. Glowworm swarm optimization
25\. Hierarchical swarm model
26\. Krill Herd
27\. Monkey Search
28\. Virtual ant algorithm
29\. Virtual bees
30\. Weighted Swarm Algorithm
31\. Wisdom of Artificial Crowd algorithm
32\. Prey-predator algorithm
33\. Memetic algorithm
34\. Lion Optimization Algorithm
35\. Chicken Swarm Optimization
36\. Ant Lion Optimizer
37\. Compact Particle Swarm Optimization
38\. Fruit Fly Optimization Algorithm
39\. marine propeller optimization algorithm
40\. The Whale Optimization Algorithm
41\. virus colony search algorithm
42\. Slime mould optimization algorithm
Ecology Inspired Algorithm
==========================
1\. Biogeography-based Optimization
2\. Invasive Weed Optimization
3\. Symbiosis-Inspired Optimization - PS2O
4\. Atmosphere Clouds Model
5\. Brain Storm Optimization
6\. Dolphin echolocation
7\. Japanese Tree Frog Calling algorithm
8\. Eco-inspired evolutionary algorithm
9\. Egyptian Vulture
10\. Fish School search
11\. Flower Pollination algorithm
12\. Gene Expression
13\. Great Salmon Run
14\. Group Search Optimizer
15\. Human Inspired Algorithm
16\. Roach Infestation algorithm
17\. Queen-bee algorithm
18\. Shuffled frog leaping algorithm
19\. Forest Optimization Algorithm
20\. coral reefs optimization algorithm
21\. cultural evolution algorithm
22\. Grey Wolf Optimizer
23\. probabilistic pso
24\. omicron aco algorithm
25\. shark smell optimization
26\. social spider algorithm
27\. sosial insects behavior algorithm
28\. sperm whale algorithm
Evolutionary Optimization
=========================
1\. Genetic Algorithm
2\. Genetic Programming
3\. Evolutionary Strategies
4\. Differential Evolution
5\. Paddy Field Algorithm
6\. Queen-bee Evolution
7\. Quantum Inspired Social Evolution
Physic and Chemistry inspired algorithm
=======================================
1\. Big bang-Big Crunch
2\. Block hole algorithm
3\. Central force optimization
4\. Charged System search
5\. Electro-magnetism optimization
6\. Galaxy based search algorithm
7\. Gravitational search
8\. Harmony search algorithm
9\. Intelligent water drop algorithm
10\. River formation algorithm
11\. Self-propelled dynamics
12\. Simulated Annealing
13\. Stachastic diffusion search
14\. Spiral optimization
15\. Water Cycle algorithm
16\. Artificial Physics optimization
17\. Binary Gravitational search algorithm
18\. Continous quantum ant colony optimization
19\. Extended artificial physics optimization
20\. Extended Central force optimization
21\. Electromagnetism-like heuristic
22\. Gravitational Interaction optimization
23\. Hysteristetic Optimization algorithm
24\. Hybrid quantum-inspired GA
25\. Immune gravitational inspired algorithm
26\. Improved quantum evolutinary algorithm
27\. Linear programming
28\. Quantum-inspired bacterial swarming
29\. Quantum-inspired evolutionary algorithm
30\. Quantum-inspired genetic algorithm
31\. Quantum-behaved PSO
32\. Unified big bang-chaotic big crunch
33\. Vector model of artificial physics
34\. Versatile quantum-inspired evolutionary algorithm
35\. Space Gravitational Algorithm
36\. Ion Motion Algorithm
37\. Light Ray Optimization Algorithm
38\. Ray Optimization
39\. Photosynthetic Algorithms
40\. floorplanning algorithm
41\. Gases Brownian Motion Optimization
42\. gradient-type optimization
43\. mean-variance optimization
44\. Mine blast algorithm
45\. moth flame optimization
46\. multi battalion search algorithm
47\. music inspired optimization
48\. no free lunch theorems algorithm
49\. Optics inspired optimization
50\. runner-root algorithm
51\. sine cosine algorithm
52\. pitch tracking algorithm
53\. Stochastic Fractal Search algorithm
54\. stroke volume optimization
55\. Stud krill herd algorithm
56\. The Great Deluge Algorithm
57\. Water Evaporation Optimization
58\. water wave optimization algorithm
59\. Island model algorithm
60\. Steady State model
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# IoT
install, setup, config jetson nano with opencv ai kit (depth camera) OAK-D
install and setup visual studio code server in jetson nano and use it on iPad
by ssh
config vscode to run CUDA
VSCode on iPad Pro - Full Setup Guide with Jetson Nano
[https://www.pirahansiah.com/topics-and-projects/source-
code/iot](https://www.pirahansiah.com/topics-and-projects/source-code/iot)
#iPad #VSCode #IoT #ssh #Jetson #CUDA #C++
www.pirahansiah.com March 2023
https://www.pirahansiah.com/topics-and-projects/source-code/iot
VSCode on iPad Pro - Full Setup Guide with Jetson Nano
// Install VSCode server
sudo apt-get install curl
curl -fsSL https://deb.nodesource.com/setup_16.x | sudo -E bash -
sudo apt-get update && sudo apt-get install yarn
Y
sudo apt-get install -y nodejs
sudo npm --force install -g yarn
yarn global add code-server
~/.yarn/bin/code-server
// config VSCode server to use by other device like iPad, ... in local network
~/.yarn/bin/code-server
gedit ~/.config/code-server/config.yaml
bind-addr: 0.0.0.0:8080
auth: none
password: pirahansiah
cert: false
sudo gedit /etc/systemd/system/code-server.service
[Unit]
Description=code-server
After=network.target
[Service]
User=pi
Group=pi
WorkingDirectory=/home/pi
Environment="PATH=/usr/bin"
ExecStart=/home/pirahansiah/.yarn/bin/code-server
[Install]
WantedBy=multi-user.target
sudo systemctl stop code-server
~/.yarn/bin/code-server
// just run this command after restart device
~/.yarn/bin/code-server
http://pirahansiah.local:8080/ => name of your device
// CUDA config for VSCode
// https://www.pirahansiah.com/topics-and-projects/source-code/iot
open foldr in VSCode code-server .
// web python testing
sudo apt-get install chromium-chromedriver
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import time
# Launch the browser and open the website
driver = webdriver.Chrome()
driver.get("https://www.pirahansiah.com/")
# Save a screenshot of the page
driver.save_screenshot("pirahansiah.png")
# Close the browser
driver.quit()
sudo apt install virtualenv
virtualenv --python=python3.8 pirahansiah
source pirahansiah/bin/activate
sudo apt-get install python3.8
sudo rm /usr/bin/python3
sudo ln -s /usr/bin/python3.8 /usr/bin/python3
sudo apt-get --reinstall install python3-minimal
tasks.json
{
"version": "2.0.0",
"tasks": [
{
"label": "build",
"type": "shell",
"command": "/usr/local/cuda/bin/nvcc",
"args": [
"-gencode",
"arch=compute_53,code=sm_53",
"-I${workspaceFolder}",
"-o",
"${workspaceFolder}/${fileBasenameNoExtension}",
"${file}",
"-g"
],
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "shared"
},
"problemMatcher": {
"owner": "cpp",
"fileLocation": ["absolute"],
"pattern": {
"regexp": "^(.*):(\\\d+):(\\\d+):\\\s+(warning|error):\\\s+(.*)$",
"file": 1,
"line": 2,
"column": 3,
"severity": 4,
"message": 5
}
}
}
]
}
launch.json
{
"version": "0.2.0",
"configurations": [
{
"name": "(gdb) Launch",
"type": "node",
"request": "launch",
"cwd": "${workspaceFolder}",
"program": "/home/farshid/code/vscode-test1/testCUDA",
"args": [],
"stopOnEntry": false,
"runtimeExecutable": "/usr/bin/gdb",
"runtimeArgs": [
"\--interpreter=mi2",
"-ex", "set confirm off",
"-ex", "tui enable",
"-ex", "set startup-with-shell off",
"-ex", "set substitute-path /usr/share/gdb /usr/local/cuda/bin",
"-ex", "file ${workspaceFolder}/${fileBasenameNoExtension}",
"-ex", "run",
"\--quiet"
],
"env": {},
"console": "integratedTerminal",
"preLaunchTask": "build"
}
]
}
c_cpp_properties.json
{
"configurations": [
{
"name": "Jetson Nano - Debug",
"includePath": [
"${workspaceFolder}/**"
],
"defines": [],
"compilerPath": "/usr/local/cuda/bin/nvcc",
"cStandard": "c11",
"cppStandard": "c++17",
"intelliSenseMode": "gcc-x64",
"compilerArgs": [
"-g",
"-O0",
"\--compiler-options",
"-Wall",
"-Wextra",
"-Wpedantic",
"-Wno-deprecated-gpu-targets"
]
}
],
"version": 4
}
[https://www.youtube.com/watch?v=11YfaGi0Fpk](https://www.youtube.com/watch?v=11YfaGi0Fpk)
[https://techcraft.co/videos/2022/2/vscode-on-ipad-pro-full-setup-guide-with-
raspberry-pi/](https://techcraft.co/videos/2022/2/vscode-on-ipad-pro-full-
setup-guide-with-raspberry-pi/)
00:00 VSCode on iPad Pro
00:44 Installing NodeJS
01:30 Install code-server
02:32 Default configuration
04:08 Connecting from Blink
05:53 Full screen Safari
07:10 Re-enable password authentication
07:34 Auto start code-server
09:38 Installing extensions
12:10 Secure mode
[https://canyouseeme.org](https://canyouseeme.org)
[https://www.youtube.com/watch?v=2tIUts0fyFk](https://www.youtube.com/watch?v=2tIUts0fyFk)
[https://whatismyipaddress.com/i](https://whatismyipaddress.com/i)
[https://www.youtube.com/watch?v=ZKfnGqMrnug](https://www.youtube.com/watch?v=ZKfnGqMrnug)
[https://github.com/facebookresearch/llama](https://github.com/facebookresearch/llama)
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* [CPP](/topics-and-projects/source-code/opencv/cpp)
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* [FQA](/about/fqa)
[Computer Vision,
Deep Learning, Artificial superintelligence (ASI)](/home)
# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
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* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision,
Deep Learning, Artificial superintelligence (ASI)](/home)
# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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[Computer Vision,
Deep Learning, Artificial superintelligence (ASI)](/home)
# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
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* [Home](/home)
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* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision,
Deep Learning, Artificial superintelligence (ASI)](/home)
# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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[Computer Vision,
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# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
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* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
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* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
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* [Links](/links)
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* [About](/about)
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Deep Learning, Artificial superintelligence (ASI)](/home)
# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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[Computer Vision, Deep
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# compile and setup source code
sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken
========================
sudo nano ~/.bashsrc
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
===============
nvcc -std=c++17 -arch=sm_60 test.cu
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
check S3 bucket in AWS for image and video files and versioning
Check Docker load balancer, memory usage, ...
GPU
Video Tracking on Mac
1. create conda based on python 3.6
* conda create env_full -y \--name farshid python=3.6
* conda activate farshid
2. install OpenVino from Intel for converting deep learning model based on intel chips
* conda install -y openvino-ie4py -c intel
3. install video library
* conda install -y -c conda-forge ffmpeg
4. install pytorch and torchvision
* conda install -y pytorch torchvision -c pytorch
5. conda install -y -c conda-forge matplotlib
6. conda install -y pandas scikit-learn plotly
7. conda install -y -c conda-forge opencv seaborn
8. conda install -y -c conda-forge tensorflow
pip install torch torchvision torchaudio
pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow
#
Test for 2021
**3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020,
ECCVW 2020)[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)
****
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images
in the Wild[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD-
ts8sZsK3X32Hb2FD)
This repository contains the public release of the Python implementation of
our Aggregate View Object Detection (AVOD) network for 3D object detection.[
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)
𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍
#
Run on Ubuntu PC + eGPU
apt search nvidia-driver
apt-cache search nvidia-driver
sudo apt update
sudo apt upgrade
sudo apt install nvidia-driver-455
sudo reboot
nvidia-smi
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
* tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
* sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
* sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
* sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
* sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb
* sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb
*
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
Make sure you have installed the NVIDIA driver and Docker engine for your
Linux distribution Note that you do not need to install the CUDA Toolkit on
the host system, but the NVIDIA driver needs to be installed
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add
- \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-
docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
curl -s -L https://nvidia.github.io/nvidia-container-
runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee
/etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
[Installing on CentOS 8
(AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud-
native%2Fcontainer-toolkit%2Finstall-
guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q)
pip install cython; pip install -U
'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)'
CenterTrack_ROOT=/home/farshid/code/CenterTrack
git clone --recursive https://github.com/xingyizhou/CenterTrack
$CenterTrack_ROOT
cd CenterTrack_ROOT
pip install -r requirements.txt
cd $CenterTrack_ROOT/src/lib/model/networks/
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh
[https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb)
#
cvat
sudo groupadd docker
sudo usermod -aG docker $USER
sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools
sudo python3 -m pip install setuptools docker-compose
sudo apt-get --no-install-recommends install -y git
git clone https://github.com/opencv/cvat
cd cvat
sudo docker-compose build
sudo docker-compose up -d
sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser'
[http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa)
#
Towards-Realtime-MOT
* conda activate cuda100
* pip install motmetrics
* pip install cython_bbox
* conda install -c conda-forge ffmpeg
*
[https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi-
Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM-
qU7ZrLmr)
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
git clone https://gitlab.inria.fr/yixu/deepmot.git
sudo apt-get install libpng-dev
sudo apt install libfreetype6-dev
pip install -r requirements.txt
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.
conda create -y --name cuda92 python=3.6
conda activate cuda92
source activate cuda92
conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch
conda install -c conda-forge ffmpeg
* conda create -n cuda100
* conda activate cuda100
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
#
[https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki)
AWS
#
AWS
##
[Towards-Realtime-MOT
](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN)
* conda create -n FairMOT
* conda activate FairMOT
* conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
* cd ${FAIRMOT_ROOT}
* pip install -r requirements.txt
* conda install -c conda-forge ffmpeg
*
[MOTS: Multi-Object Tracking and
Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix)
* Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t)
* Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo)
* This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol.
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
Setup:
* cd /media/farshid/exfat128/code
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack
* conda install pytorch torchvision -c pytorch
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
*
#
AWS (11 December 2020)
[https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD)
* Conda
* conda create --name CenterTrack36cuda10 python=3.6
* conda activate CenterTrack36cuda10
* conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
* conda install -c conda-forge ffmpeg
* pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
* CenterTrack_ROOT=/
* git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT
* pip install -r requirements.txt
* cd $CenterTrack_ROOT/src/lib/model/networks/
* git clone https://github.com/CharlesShang/DCNv2/
* cd DCNv2
* ./make.sh
* Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb).
* ###
Training
* cd $CenterTrack_ROOT/src/tools/
* bash get_mot_17.sh
*
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* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
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* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# Rust
curl --proto '=https' \--tlsv1.2 -sSf https://sh.rustup.rs | sh
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* [FSDL](/courses/fsdl)
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* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
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# MacOS+OpenCV
[https://www.pirahansiah.com/topics-and-projects/source-code/opencv/macos-
opencv](https://www.pirahansiah.com/topics-and-projects/source-code/opencv/macos-
opencv)
YouTube Channel:
[https://www.youtube.com/c/ComputerVisionDeepLearning](https://www.youtube.com/c/ComputerVisionDeepLearning)
OpenCV on MacOS - 2023
How to compile OpenCV on Mac
How to use OpenCV with Xcode (C++)
To view these steps, you may watch a video on YouTube.
[https://youtu.be/dgYy4Cf1qO4](https://youtu.be/dgYy4Cf1qO4)
* /bin/bash -c "$(curl -fsSL [https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh](https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh))"
* brew install jpeg libpng libtiff openexr
* brew install opencv
* * find path of OpenCV. in order to see hidden folders and file in Mac you can use "Command+Shift+Dot"
* export PKG_CONFIG_PATH="/usr/local/Cellar/opencv/4.7.0_1/lib/pkgconfig:$PKG_CONFIG_PATH"
* pkg-config --cflags opencv4
* s
* Makefile
TARGET = ./main
SRCS := $(wildcard ./src/*.cpp ./*.cpp)
OBJS := $(patsubst %cpp,%o,$(SRCS))
CFLG = -g -Wall -I/usr/local/Cellar/opencv/4.7.0_1/include/opencv4 -Iinc -I./
-std=c++17
LDFG = -Wl, $(shell pkg-config opencv --cflags --libs)
CXX = g++
$(TARGET) : $(OBJS)
$(CXX) -o $(TARGET) $(OBJS) $(LDFG)
%.o:%.cpp
$(CXX) $(CFLG) -c $< -o $@
clean:
-rm ./*.o
* tasks.json
{
"version": "2.0.0",
"tasks": [
{
"label": "Build",
"type": "shell",
"command": "g++",
"args": [
"-std=c++17",
"${file}",
"-o",
"${fileDirname}/${fileBasenameNoExtension}.out",
"-I",
"/usr/local/Cellar/opencv/4.7.0_1/include/opencv4/opencv2",
"-I",
"/usr/local/Cellar/opencv/4.7.0_1/include/opencv4",
"-L",
"/usr/local/Cellar/opencv/4.7.0_1/lib",
"-l",
"opencv_stitching",
"-l",
"opencv_superres",
"-l",
"opencv_videostab",
"-l",
"opencv_aruco",
"-l",
"opencv_bgsegm",
"-l",
"opencv_bioinspired",
"-l",
"opencv_ccalib",
"-l",
"opencv_dnn_objdetect",
"-l",
"opencv_dpm",
"-l",
"opencv_face",
"-l",
"opencv_fuzzy",
"-l",
"opencv_hfs",
"-l",
"opencv_img_hash",
"-l",
"opencv_line_descriptor",
"-l",
"opencv_optflow",
"-l",
"opencv_reg",
"-l",
"opencv_rgbd",
"-l",
"opencv_saliency",
"-l",
"opencv_stereo",
"-l",
"opencv_structured_light",
"-l",
"opencv_phase_unwrapping",
"-l",
"opencv_surface_matching",
"-l",
"opencv_tracking",
"-l",
"opencv_datasets",
"-l",
"opencv_dnn",
"-l",
"opencv_plot",
"-l",
"opencv_xfeatures2d",
"-l",
"opencv_shape",
"-l",
"opencv_video",
"-l",
"opencv_ml",
"-l",
"opencv_ximgproc",
"-l",
"opencv_xobjdetect",
"-l",
"opencv_objdetect",
"-l",
"opencv_calib3d",
"-l",
"opencv_features2d",
"-l",
"opencv_highgui",
"-l",
"opencv_videoio",
"-l",
"opencv_imgcodecs",
"-l",
"opencv_flann",
"-l",
"opencv_xphoto",
"-l",
"opencv_photo",
"-l",
"opencv_imgproc",
"-l",
"opencv_core",
"-g"
],
"group": {
"kind": "build",
"isDefault": true
},
"problemMatcher": [
"$gcc"
]
}
]
}
* launch.json
{
"version": "0.2.0",
"configurations": [
{
"name": "(lldb) Launch",
"type": "cppdbg",
"request": "launch",
"program": "${fileDirname}/${fileBasenameNoExtension}.out",
"args": [],
"stopAtEntry": true,
"cwd": "${workspaceFolder}",
"environment": [],
"externalConsole": true,
"MIMode": "lldb",
"preLaunchTask": "Build"
}
]
}
* if you do not want external terminal (you want to use internal terminal in vscode) you may change the line
* "externalConsole": false
* if you do not want debug and run line by line the code and not stop in first line of code (F10 or F5 to continue) you may change the line to
* "stopAtEntry": false
* c_cpp_properties.json
{
"configurations": [
{
"name": "Mac",
"includePath": [
"${workspaceFolder}/**",
"/usr/local/Cellar/opencv/4.7.0_1/include/opencv4",
"/usr/local/Cellar/opencv/4.7.0_1/include"
],
"defines": [],
"macFrameworkPath": [],
"compilerPath": "/usr/bin/g++",
"cStandard": "c17",
"cppStandard": "c++17",
"intelliSenseMode": "clang-x64",
"browse": {
"path": [
"/usr/local/Cellar/opencv/4.7.0_1/include/opencv4"
],
"limitSymbolsToIncludedHeaders": true,
"databaseFilename": ""
}
}
],
"version": 4
}
* /bin/bash -c "$(curl -fsSL [https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh](https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh))"
* brew install jpeg libpng libtiff openexr
* brew install opencv
* * export PKG_CONFIG_PATH="/usr/local/Cellar/opencv/4.7.0_1/lib/pkgconfig:$PKG_CONFIG_PATH"
* pkg-config --cflags opencv4
* in VS code add below into c_cpp_properties.json file in .vscode
* * "includePath": [ ... "/usr/local/opt/opencv/include/opencv4" ...
Command+Shift+Dot.
* /usr/local/Cellar/opencv/4.7.0_1/include/opencv4/**
* copy .dylib files from lib folder (you can copy directly into your project or create folder and copy there)
#include
#include "opencv2/opencv.hpp"
using namespace cv;
using namespace std;
int main(int argc, const char * argv[]) {
// insert code here...
cout << "OpenCV version : " << CV_VERSION << endl;
cout << "Major version : " << CV_MAJOR_VERSION << endl;
cout << "Minor version : " << CV_MINOR_VERSION << endl;
cout << "Subminor version : " << CV_SUBMINOR_VERSION << endl;
std::cout << "Hello, World!\n";
return 0;
}
To learn how to install OpenCV (C++) on a MacOS and utilize it in an Xcode
project with a simple configuration, you can watch a video on YouTube. The
video will include a download link, source code, and additional documents for
reference. It is scheduled to be released in 2023.
OpenCV (C++) is a popular computer vision library that allows developers to
perform various image and video processing tasks such as object detection,
face recognition, and image segmentation. Using OpenCV on a MacOS provides
developers with a stable and reliable platform to build their computer vision
applications. Additionally, MacOS has a user-friendly interface and powerful
development tools, such as Xcode, which can help streamline the development
process. Overall, utilizing OpenCV on a MacOS can help developers create high-
performance computer vision applications with ease.
Metal is a low-level graphics framework developed by Apple that can be used in
conjunction with OpenCV (C++) on a MacOS. Metal provides a high-performance
computing environment for developers to process large amounts of data while
minimizing CPU usage. By combining the power of Metal with the feature-rich
OpenCV library, developers can create high-performance computer vision
applications that are capable of processing large amounts of data in real-
time.
Metal's efficient graphics pipeline and parallel processing capabilities make
it ideal for use in computer vision applications. It provides a high level of
performance and scalability for tasks such as image and video processing,
object recognition, and machine learning. Additionally, Metal's seamless
integration with the Xcode development environment makes it easy to use in
combination with OpenCV for MacOS application development. Overall, using
Metal with OpenCV (C++) on a MacOS can help developers create high-performance
and efficient computer vision applications.
1. Real-time object detection: With the help of OpenCV's object detection algorithms and Metal's parallel processing capabilities, developers can build an application that can detect and track objects in real-time video streams.
2. Image segmentation: Image segmentation is the process of dividing an image into different regions or segments, each of which represents a different object or part of the image. Using OpenCV with Metal, developers can build an application that can perform image segmentation in real-time, making it useful for various applications such as medical imaging.
3. Facial recognition: With OpenCV's facial recognition algorithms and Metal's parallel processing capabilities, developers can build a facial recognition system that can quickly and accurately identify people in real-time.
4. Machine learning: OpenCV provides a rich set of machine learning tools, and Metal's parallel processing capabilities can be used to train and run machine learning models in real-time.
Overall, the combination of OpenCV with Metal on a MacOS provides developers
with a powerful platform to build a wide range of computer vision
applications.
* brew install pkg-config
* export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig
Compile 1:
sudo xcodebuild -license
sudo xcode-select --install
/usr/bin/ruby -e "$(curl -fsSL
[https://raw.githubusercontent.com/Homebrew/install/master/install](https://raw.githubusercontent.com/Homebrew/install/master/install))"
* brew install cmake pkg-config
* brew install jpeg libpng libtiff openexr
* brew install wget
* brew install --cask yuna
* sudo ./cmake-gui
*
brew install cmake
brew install --cask cmake
cd ~/
git clone https://github.com/opencv/opencv.git
git clone
[https://github.com/opencv/opencv_contrib.git](https://github.com/opencv/opencv_contrib.git)
* git branch -a
* git switch 5.x
mkdir build_opencv
cd build_opencv
cmake gui
* opencv flolder
* opencv build folder
* Unix makefile compiler ( do not select Xcode)
* OPENCV_EXTRA_MODULES_PATH to /modules
* OPENCV_ENABLE_NONFREE=ON
* configure again
* generate
* remove below items
* zlib
* Java = 2x
* imgcode
* ipp = 2x
* xfeatures2d
* face
* wechat qrcode
* imgproc
* ade
*
make -j8
sh setup_vars.sh
sudo make install
Compile 3:
sudo xcodebuild -license
sudo xcode-select --install
/usr/bin/ruby -e "$(curl -fsSL
[https://raw.githubusercontent.com/Homebrew/install/master/install](https://raw.githubusercontent.com/Homebrew/install/master/install))"
* brew install cmake pkg-config
* brew install jpeg libpng libtiff openexr
* brew install wget
* brew install --cask yuna
* sudo ./cmake-gui
*
brew install cmake
brew install --cask cmake
cd ~/
git clone https://github.com/opencv/opencv.git
git clone
[https://github.com/opencv/opencv_contrib.git](https://github.com/opencv/opencv_contrib.git)
* git branch -a
* git switch 5.x
mkdir build_opencv
cd build_opencv
cmake
* opencv flolder
* opencv build folder
* Unix makefile compiler ( do not select Xcode)
* OPENCV_EXTRA_MODULES_PATH to /modules
* OPENCV_ENABLE_NONFREE=ON
* configure again
* generate
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D OPENCV_EXTRA_MODULES_PATH=/Users/farshid/code/opencv_contrib/modules \
-D PYTHON3_LIBRARY=`python -c 'import subprocess ; import sys ; s = subprocess.check_output("python-config --configdir", shell=True).decode("utf-8").strip() ; (M, m) = sys.version_info[:2] ; print("{}/libpython{}.{}.dylib".format(s, M, m))'` \
-D PYTHON3_INCLUDE_DIR=`python -c 'import distutils.sysconfig as s; print(s.get_python_inc())'` \
-D PYTHON3_EXECUTABLE=$VIRTUAL_ENV/bin/python \
-D BUILD_opencv_python2=OFF \
-D BUILD_opencv_python3=ON \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D INSTALL_C_EXAMPLES=OFF \
-D OPENCV_ENABLE_NONFREE=ON \
-D BUILD_EXAMPLES=ON ../opencv
rm CMakeCache.txt
make -j8
sh setup_vars.sh
sudo make install
Compile:
brew install cmake
brew install --cask cmake
cd ~/
git clone https://github.com/opencv/opencv.git
git clone
[https://github.com/opencv/opencv_contrib.git](https://github.com/opencv/opencv_contrib.git)
* git branch -a
* git switch 5.x
mkdir build_opencv
cd build_opencv
cmake gui
* opencv flolder
* opencv build folder
* Unix makefile compiler ( do not select Xcode)
* OPENCV_EXTRA_MODULES_PATH to /modules
* configure again
* generate
* remove below items
* zlib
* Java = 2x
* imgcode
* ipp = 2x
* xfeatures2d
* face
* wechat qrcode
* imgproc
* ade
*
make -j8
sh setup_vars.sh
sudo make install
brew install pkg-config
/usr/local/include/opencv5/**
[https://anuragajwani.medium.com/how-to-develop-an-opencv-c-algorithm-in-
xcode-d676b9aad1b7](https://anuragajwani.medium.com/how-to-develop-an-opencv-
c-algorithm-in-xcode-d676b9aad1b7)
[https://dev.to/0xkoji/use-opencv-with-xcode-41n0](https://dev.to/0xkoji/use-
opencv-with-xcode-41n0)
[https://pyimagesearch.com/2018/08/17/install-opencv-4-on-
macos/](https://pyimagesearch.com/2018/08/17/install-opencv-4-on-macos/)
[https://github.com/angel-
star/vscode_OpenCV_template_for_Mac](https://github.com/angel-
star/vscode_OpenCV_template_for_Mac)
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traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
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* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
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* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Python
##
##
global _
folder, file name, functions,
const,
##
doc
read_all_image_in_folder
recursive = True
##
code
##
doc
manual progress bar for python based on number of images process.
##
code
##
doc
this code shows information about image
##
code
import unittest
for the functions: for example one time create file and then detected in
between all operations in this function
def setUp(self):
def tearDown(self)
for the class:
@classmethod
def setUpClass(cls):
@classmethod
def tearDownClass(cls):
#
List files and folders
If you want to list directories which shows specific folder name in path in
windows you can use
dir /s /b /o:n /ad "farshid" > farshid.txt
this command listed all directories which have "farshid" in the path and save
it to the farshid.txt file
in python you can use below code to search and find specific folders and files
######################################################################################
import
import os
import glob
######################################################################################
config
root="C:\\\farshid\\\"
specific_directories=root+"/**/farshid/**/*.jpg"
path_dir_detection_check=""
######################################################################################
function
files= glob.glob(specific_directories, recursive=True)
for file in files:
b=file.rfind("farshid")
path_dir_detection=file[0:b-1]
if (path_dir_detection != path_dir_detection_check):
dirname = os.path.dirname(file)
print("******************************************************* Next
Directories ************************")
print(dirname)
path_dir_detection_check=path_dir_detection
print(file)
The source code can be found in
[GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr)
make -j$(sysctl -n hw.physicalcpu)
shift+enter -> run selection
menue
code-> pereferences -> user snippets -> python.json
pip freeze > requirements.txt
extensions
Visual Studio IntelliCode
SSH FS
ext install Kelvin.vscode-sshfs
command+ shift+ p ->SSH SF: Create new SSH SF configuration
code ~/.zshrc
Bracket Pair Colorizer 2 => color (){}[] different color
Prettier - Code formatter => when you save. setting->(format on save)
indent-rainbow
shell => .code
Compare Folders
Command +p = all files -> if I press alt+ it open new tab the file
control + ` = open terminal
Command + o= open folder
Command + , = open setting
Command + /= #
shift+enter=run one line of code
option+shift+arrow down = duplicate line in code
command+ click mouse => go to function
command++ -> bigger
command+ shift +P
command+ K , command+ S => shourcuts
command+ L => select currnt line
command+ left/right arrow => start or end of line
command+ P => go to file in search
git config --global core.excludesfile ~/.gitignore
code ~/.gitignore
brew install pyenv
brew install poetry
pyenv install 3.7.5
pyenv global 3.7.5
poetry new "name of project"
-> go to folder
-> change python version if you want in the pyproject.toml
pyenv global 3.7.5
poetry new "pytorch_pretrained"
poetry install
pip install --upgrade pip
poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv-
python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil
seldon_core spacy sklearn torch torchvision jupyter pycocotools cython
pyyaml==5.1
poetry remove torch torchvision
pip install --pre torch torchvision -f[
](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)
pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0
opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python-
dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools
cython
pip3 install pyyaml==5.1
pip3 install 'git+https://github.com/facebookresearch/detectron2.git'
pip3 install -U
'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
poetry shell
jupyter notebook
pyenv
pyenv install
pyenv install 3.7.5
cd folder
pyenv global 3.7.5
pyenv versions
python -> you can see this environments
poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc
poetry run which python
poetry run jupyter lab
pipenv install requests
pyenv virtualenvs cv-endpoint
pyenv activate cv-endpoint
====================
black python
[https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1)
The Uncompromising Code Formatter
pip install black
====================
pre-commit A framework for managing and maintaining multi-language pre-commit
hooks.
pip install pre-commit
brew install pre-commit
.pre-commit-config.yaml
repos:
\- repo:
[https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R)
rev: v1.8.0
hooks:
\- id: reorder-python-imports
exclude: notebooks/
language_version: python3.7
\- repo:
[https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto)
rev: 19.10b0
hooks:
\- id: black
exclude: notebooks/
language_version: python3.7
\- repo: [https://github.com/pre-commit/pre-commit-
hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre-
commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi)
rev: v2.4.0
hooks:
\- id: flake8
args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max-
complexity=15', '\--select=B,C,E,F,W,T4,B9']
exclude: notebooks/
language_version: python3.7
pre-commit install
pre-commit run --all-files
git
make file
make check
code .
**add path**
import sys
sys.path.append(r'C: )
**create Mat**
bin_im = np.zeros((5,16))
bin_im = bin_im.astype(np.uint8)*255
**contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n',
header='', footer='', comments='# ')
**DLL**
import ctypes
my_dll = r"C:\fffffff.dll"
lib = ctypes.windll.LoadLibrary(my_dll)
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
**read matlab mat file to python**
from scipy import io
res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False,
squeeze_me=True)
for k in res.keys():
print(k, res[k])
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
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traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Python
##
##
global _
folder, file name, functions,
const,
##
doc
read_all_image_in_folder
recursive = True
##
code
##
doc
manual progress bar for python based on number of images process.
##
code
##
doc
this code shows information about image
##
code
import unittest
for the functions: for example one time create file and then detected in
between all operations in this function
def setUp(self):
def tearDown(self)
for the class:
@classmethod
def setUpClass(cls):
@classmethod
def tearDownClass(cls):
#
List files and folders
If you want to list directories which shows specific folder name in path in
windows you can use
dir /s /b /o:n /ad "farshid" > farshid.txt
this command listed all directories which have "farshid" in the path and save
it to the farshid.txt file
in python you can use below code to search and find specific folders and files
######################################################################################
import
import os
import glob
######################################################################################
config
root="C:\\\farshid\\\"
specific_directories=root+"/**/farshid/**/*.jpg"
path_dir_detection_check=""
######################################################################################
function
files= glob.glob(specific_directories, recursive=True)
for file in files:
b=file.rfind("farshid")
path_dir_detection=file[0:b-1]
if (path_dir_detection != path_dir_detection_check):
dirname = os.path.dirname(file)
print("******************************************************* Next
Directories ************************")
print(dirname)
path_dir_detection_check=path_dir_detection
print(file)
The source code can be found in
[GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr)
make -j$(sysctl -n hw.physicalcpu)
shift+enter -> run selection
menue
code-> pereferences -> user snippets -> python.json
pip freeze > requirements.txt
extensions
Visual Studio IntelliCode
SSH FS
ext install Kelvin.vscode-sshfs
command+ shift+ p ->SSH SF: Create new SSH SF configuration
code ~/.zshrc
Bracket Pair Colorizer 2 => color (){}[] different color
Prettier - Code formatter => when you save. setting->(format on save)
indent-rainbow
shell => .code
Compare Folders
Command +p = all files -> if I press alt+ it open new tab the file
control + ` = open terminal
Command + o= open folder
Command + , = open setting
Command + /= #
shift+enter=run one line of code
option+shift+arrow down = duplicate line in code
command+ click mouse => go to function
command++ -> bigger
command+ shift +P
command+ K , command+ S => shourcuts
command+ L => select currnt line
command+ left/right arrow => start or end of line
command+ P => go to file in search
git config --global core.excludesfile ~/.gitignore
code ~/.gitignore
brew install pyenv
brew install poetry
pyenv install 3.7.5
pyenv global 3.7.5
poetry new "name of project"
-> go to folder
-> change python version if you want in the pyproject.toml
pyenv global 3.7.5
poetry new "pytorch_pretrained"
poetry install
pip install --upgrade pip
poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv-
python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil
seldon_core spacy sklearn torch torchvision jupyter pycocotools cython
pyyaml==5.1
poetry remove torch torchvision
pip install --pre torch torchvision -f[
](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)
pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0
opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python-
dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools
cython
pip3 install pyyaml==5.1
pip3 install 'git+https://github.com/facebookresearch/detectron2.git'
pip3 install -U
'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
poetry shell
jupyter notebook
pyenv
pyenv install
pyenv install 3.7.5
cd folder
pyenv global 3.7.5
pyenv versions
python -> you can see this environments
poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc
poetry run which python
poetry run jupyter lab
pipenv install requests
pyenv virtualenvs cv-endpoint
pyenv activate cv-endpoint
====================
black python
[https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1)
The Uncompromising Code Formatter
pip install black
====================
pre-commit A framework for managing and maintaining multi-language pre-commit
hooks.
pip install pre-commit
brew install pre-commit
.pre-commit-config.yaml
repos:
\- repo:
[https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R)
rev: v1.8.0
hooks:
\- id: reorder-python-imports
exclude: notebooks/
language_version: python3.7
\- repo:
[https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto)
rev: 19.10b0
hooks:
\- id: black
exclude: notebooks/
language_version: python3.7
\- repo: [https://github.com/pre-commit/pre-commit-
hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre-
commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi)
rev: v2.4.0
hooks:
\- id: flake8
args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max-
complexity=15', '\--select=B,C,E,F,W,T4,B9']
exclude: notebooks/
language_version: python3.7
pre-commit install
pre-commit run --all-files
git
make file
make check
code .
**add path**
import sys
sys.path.append(r'C: )
**create Mat**
bin_im = np.zeros((5,16))
bin_im = bin_im.astype(np.uint8)*255
**contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n',
header='', footer='', comments='# ')
**DLL**
import ctypes
my_dll = r"C:\fffffff.dll"
lib = ctypes.windll.LoadLibrary(my_dll)
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
**read matlab mat file to python**
from scipy import io
res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False,
squeeze_me=True)
for k in res.keys():
print(k, res[k])
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
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* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
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[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Python
##
##
global _
folder, file name, functions,
const,
##
doc
read_all_image_in_folder
recursive = True
##
code
##
doc
manual progress bar for python based on number of images process.
##
code
##
doc
this code shows information about image
##
code
import unittest
for the functions: for example one time create file and then detected in
between all operations in this function
def setUp(self):
def tearDown(self)
for the class:
@classmethod
def setUpClass(cls):
@classmethod
def tearDownClass(cls):
#
List files and folders
If you want to list directories which shows specific folder name in path in
windows you can use
dir /s /b /o:n /ad "farshid" > farshid.txt
this command listed all directories which have "farshid" in the path and save
it to the farshid.txt file
in python you can use below code to search and find specific folders and files
######################################################################################
import
import os
import glob
######################################################################################
config
root="C:\\\farshid\\\"
specific_directories=root+"/**/farshid/**/*.jpg"
path_dir_detection_check=""
######################################################################################
function
files= glob.glob(specific_directories, recursive=True)
for file in files:
b=file.rfind("farshid")
path_dir_detection=file[0:b-1]
if (path_dir_detection != path_dir_detection_check):
dirname = os.path.dirname(file)
print("******************************************************* Next
Directories ************************")
print(dirname)
path_dir_detection_check=path_dir_detection
print(file)
The source code can be found in
[GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr)
make -j$(sysctl -n hw.physicalcpu)
shift+enter -> run selection
menue
code-> pereferences -> user snippets -> python.json
pip freeze > requirements.txt
extensions
Visual Studio IntelliCode
SSH FS
ext install Kelvin.vscode-sshfs
command+ shift+ p ->SSH SF: Create new SSH SF configuration
code ~/.zshrc
Bracket Pair Colorizer 2 => color (){}[] different color
Prettier - Code formatter => when you save. setting->(format on save)
indent-rainbow
shell => .code
Compare Folders
Command +p = all files -> if I press alt+ it open new tab the file
control + ` = open terminal
Command + o= open folder
Command + , = open setting
Command + /= #
shift+enter=run one line of code
option+shift+arrow down = duplicate line in code
command+ click mouse => go to function
command++ -> bigger
command+ shift +P
command+ K , command+ S => shourcuts
command+ L => select currnt line
command+ left/right arrow => start or end of line
command+ P => go to file in search
git config --global core.excludesfile ~/.gitignore
code ~/.gitignore
brew install pyenv
brew install poetry
pyenv install 3.7.5
pyenv global 3.7.5
poetry new "name of project"
-> go to folder
-> change python version if you want in the pyproject.toml
pyenv global 3.7.5
poetry new "pytorch_pretrained"
poetry install
pip install --upgrade pip
poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv-
python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil
seldon_core spacy sklearn torch torchvision jupyter pycocotools cython
pyyaml==5.1
poetry remove torch torchvision
pip install --pre torch torchvision -f[
](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)
pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0
opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python-
dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools
cython
pip3 install pyyaml==5.1
pip3 install 'git+https://github.com/facebookresearch/detectron2.git'
pip3 install -U
'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
poetry shell
jupyter notebook
pyenv
pyenv install
pyenv install 3.7.5
cd folder
pyenv global 3.7.5
pyenv versions
python -> you can see this environments
poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc
poetry run which python
poetry run jupyter lab
pipenv install requests
pyenv virtualenvs cv-endpoint
pyenv activate cv-endpoint
====================
black python
[https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1)
The Uncompromising Code Formatter
pip install black
====================
pre-commit A framework for managing and maintaining multi-language pre-commit
hooks.
pip install pre-commit
brew install pre-commit
.pre-commit-config.yaml
repos:
\- repo:
[https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R)
rev: v1.8.0
hooks:
\- id: reorder-python-imports
exclude: notebooks/
language_version: python3.7
\- repo:
[https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto)
rev: 19.10b0
hooks:
\- id: black
exclude: notebooks/
language_version: python3.7
\- repo: [https://github.com/pre-commit/pre-commit-
hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre-
commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi)
rev: v2.4.0
hooks:
\- id: flake8
args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max-
complexity=15', '\--select=B,C,E,F,W,T4,B9']
exclude: notebooks/
language_version: python3.7
pre-commit install
pre-commit run --all-files
git
make file
make check
code .
**add path**
import sys
sys.path.append(r'C: )
**create Mat**
bin_im = np.zeros((5,16))
bin_im = bin_im.astype(np.uint8)*255
**contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n',
header='', footer='', comments='# ')
**DLL**
import ctypes
my_dll = r"C:\fffffff.dll"
lib = ctypes.windll.LoadLibrary(my_dll)
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
**read matlab mat file to python**
from scipy import io
res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False,
squeeze_me=True)
for k in res.keys():
print(k, res[k])
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
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* [FSDL](/courses/fsdl)
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* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Python
##
##
global _
folder, file name, functions,
const,
##
doc
read_all_image_in_folder
recursive = True
##
code
##
doc
manual progress bar for python based on number of images process.
##
code
##
doc
this code shows information about image
##
code
import unittest
for the functions: for example one time create file and then detected in
between all operations in this function
def setUp(self):
def tearDown(self)
for the class:
@classmethod
def setUpClass(cls):
@classmethod
def tearDownClass(cls):
#
List files and folders
If you want to list directories which shows specific folder name in path in
windows you can use
dir /s /b /o:n /ad "farshid" > farshid.txt
this command listed all directories which have "farshid" in the path and save
it to the farshid.txt file
in python you can use below code to search and find specific folders and files
######################################################################################
import
import os
import glob
######################################################################################
config
root="C:\\\farshid\\\"
specific_directories=root+"/**/farshid/**/*.jpg"
path_dir_detection_check=""
######################################################################################
function
files= glob.glob(specific_directories, recursive=True)
for file in files:
b=file.rfind("farshid")
path_dir_detection=file[0:b-1]
if (path_dir_detection != path_dir_detection_check):
dirname = os.path.dirname(file)
print("******************************************************* Next
Directories ************************")
print(dirname)
path_dir_detection_check=path_dir_detection
print(file)
The source code can be found in
[GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr)
make -j$(sysctl -n hw.physicalcpu)
shift+enter -> run selection
menue
code-> pereferences -> user snippets -> python.json
pip freeze > requirements.txt
extensions
Visual Studio IntelliCode
SSH FS
ext install Kelvin.vscode-sshfs
command+ shift+ p ->SSH SF: Create new SSH SF configuration
code ~/.zshrc
Bracket Pair Colorizer 2 => color (){}[] different color
Prettier - Code formatter => when you save. setting->(format on save)
indent-rainbow
shell => .code
Compare Folders
Command +p = all files -> if I press alt+ it open new tab the file
control + ` = open terminal
Command + o= open folder
Command + , = open setting
Command + /= #
shift+enter=run one line of code
option+shift+arrow down = duplicate line in code
command+ click mouse => go to function
command++ -> bigger
command+ shift +P
command+ K , command+ S => shourcuts
command+ L => select currnt line
command+ left/right arrow => start or end of line
command+ P => go to file in search
git config --global core.excludesfile ~/.gitignore
code ~/.gitignore
brew install pyenv
brew install poetry
pyenv install 3.7.5
pyenv global 3.7.5
poetry new "name of project"
-> go to folder
-> change python version if you want in the pyproject.toml
pyenv global 3.7.5
poetry new "pytorch_pretrained"
poetry install
pip install --upgrade pip
poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv-
python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil
seldon_core spacy sklearn torch torchvision jupyter pycocotools cython
pyyaml==5.1
poetry remove torch torchvision
pip install --pre torch torchvision -f[
](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)
pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0
opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python-
dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools
cython
pip3 install pyyaml==5.1
pip3 install 'git+https://github.com/facebookresearch/detectron2.git'
pip3 install -U
'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
poetry shell
jupyter notebook
pyenv
pyenv install
pyenv install 3.7.5
cd folder
pyenv global 3.7.5
pyenv versions
python -> you can see this environments
poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc
poetry run which python
poetry run jupyter lab
pipenv install requests
pyenv virtualenvs cv-endpoint
pyenv activate cv-endpoint
====================
black python
[https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1)
The Uncompromising Code Formatter
pip install black
====================
pre-commit A framework for managing and maintaining multi-language pre-commit
hooks.
pip install pre-commit
brew install pre-commit
.pre-commit-config.yaml
repos:
\- repo:
[https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R)
rev: v1.8.0
hooks:
\- id: reorder-python-imports
exclude: notebooks/
language_version: python3.7
\- repo:
[https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto)
rev: 19.10b0
hooks:
\- id: black
exclude: notebooks/
language_version: python3.7
\- repo: [https://github.com/pre-commit/pre-commit-
hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre-
commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi)
rev: v2.4.0
hooks:
\- id: flake8
args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max-
complexity=15', '\--select=B,C,E,F,W,T4,B9']
exclude: notebooks/
language_version: python3.7
pre-commit install
pre-commit run --all-files
git
make file
make check
code .
**add path**
import sys
sys.path.append(r'C: )
**create Mat**
bin_im = np.zeros((5,16))
bin_im = bin_im.astype(np.uint8)*255
**contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n',
header='', footer='', comments='# ')
**DLL**
import ctypes
my_dll = r"C:\fffffff.dll"
lib = ctypes.windll.LoadLibrary(my_dll)
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
**read matlab mat file to python**
from scipy import io
res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False,
squeeze_me=True)
for k in res.keys():
print(k, res[k])
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Python
##
##
global _
folder, file name, functions,
const,
##
doc
read_all_image_in_folder
recursive = True
##
code
##
doc
manual progress bar for python based on number of images process.
##
code
##
doc
this code shows information about image
##
code
import unittest
for the functions: for example one time create file and then detected in
between all operations in this function
def setUp(self):
def tearDown(self)
for the class:
@classmethod
def setUpClass(cls):
@classmethod
def tearDownClass(cls):
#
List files and folders
If you want to list directories which shows specific folder name in path in
windows you can use
dir /s /b /o:n /ad "farshid" > farshid.txt
this command listed all directories which have "farshid" in the path and save
it to the farshid.txt file
in python you can use below code to search and find specific folders and files
######################################################################################
import
import os
import glob
######################################################################################
config
root="C:\\\farshid\\\"
specific_directories=root+"/**/farshid/**/*.jpg"
path_dir_detection_check=""
######################################################################################
function
files= glob.glob(specific_directories, recursive=True)
for file in files:
b=file.rfind("farshid")
path_dir_detection=file[0:b-1]
if (path_dir_detection != path_dir_detection_check):
dirname = os.path.dirname(file)
print("******************************************************* Next
Directories ************************")
print(dirname)
path_dir_detection_check=path_dir_detection
print(file)
The source code can be found in
[GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr)
make -j$(sysctl -n hw.physicalcpu)
shift+enter -> run selection
menue
code-> pereferences -> user snippets -> python.json
pip freeze > requirements.txt
extensions
Visual Studio IntelliCode
SSH FS
ext install Kelvin.vscode-sshfs
command+ shift+ p ->SSH SF: Create new SSH SF configuration
code ~/.zshrc
Bracket Pair Colorizer 2 => color (){}[] different color
Prettier - Code formatter => when you save. setting->(format on save)
indent-rainbow
shell => .code
Compare Folders
Command +p = all files -> if I press alt+ it open new tab the file
control + ` = open terminal
Command + o= open folder
Command + , = open setting
Command + /= #
shift+enter=run one line of code
option+shift+arrow down = duplicate line in code
command+ click mouse => go to function
command++ -> bigger
command+ shift +P
command+ K , command+ S => shourcuts
command+ L => select currnt line
command+ left/right arrow => start or end of line
command+ P => go to file in search
git config --global core.excludesfile ~/.gitignore
code ~/.gitignore
brew install pyenv
brew install poetry
pyenv install 3.7.5
pyenv global 3.7.5
poetry new "name of project"
-> go to folder
-> change python version if you want in the pyproject.toml
pyenv global 3.7.5
poetry new "pytorch_pretrained"
poetry install
pip install --upgrade pip
poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv-
python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil
seldon_core spacy sklearn torch torchvision jupyter pycocotools cython
pyyaml==5.1
poetry remove torch torchvision
pip install --pre torch torchvision -f[
](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)
pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0
opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python-
dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools
cython
pip3 install pyyaml==5.1
pip3 install 'git+https://github.com/facebookresearch/detectron2.git'
pip3 install -U
'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
poetry shell
jupyter notebook
pyenv
pyenv install
pyenv install 3.7.5
cd folder
pyenv global 3.7.5
pyenv versions
python -> you can see this environments
poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc
poetry run which python
poetry run jupyter lab
pipenv install requests
pyenv virtualenvs cv-endpoint
pyenv activate cv-endpoint
====================
black python
[https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1)
The Uncompromising Code Formatter
pip install black
====================
pre-commit A framework for managing and maintaining multi-language pre-commit
hooks.
pip install pre-commit
brew install pre-commit
.pre-commit-config.yaml
repos:
\- repo:
[https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R)
rev: v1.8.0
hooks:
\- id: reorder-python-imports
exclude: notebooks/
language_version: python3.7
\- repo:
[https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto)
rev: 19.10b0
hooks:
\- id: black
exclude: notebooks/
language_version: python3.7
\- repo: [https://github.com/pre-commit/pre-commit-
hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre-
commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi)
rev: v2.4.0
hooks:
\- id: flake8
args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max-
complexity=15', '\--select=B,C,E,F,W,T4,B9']
exclude: notebooks/
language_version: python3.7
pre-commit install
pre-commit run --all-files
git
make file
make check
code .
**add path**
import sys
sys.path.append(r'C: )
**create Mat**
bin_im = np.zeros((5,16))
bin_im = bin_im.astype(np.uint8)*255
**contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n',
header='', footer='', comments='# ')
**DLL**
import ctypes
my_dll = r"C:\fffffff.dll"
lib = ctypes.windll.LoadLibrary(my_dll)
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
**read matlab mat file to python**
from scipy import io
res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False,
squeeze_me=True)
for k in res.keys():
print(k, res[k])
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# Python
##
##
global _
folder, file name, functions,
const,
##
doc
read_all_image_in_folder
recursive = True
##
code
##
doc
manual progress bar for python based on number of images process.
##
code
##
doc
this code shows information about image
##
code
import unittest
for the functions: for example one time create file and then detected in
between all operations in this function
def setUp(self):
def tearDown(self)
for the class:
@classmethod
def setUpClass(cls):
@classmethod
def tearDownClass(cls):
#
List files and folders
If you want to list directories which shows specific folder name in path in
windows you can use
dir /s /b /o:n /ad "farshid" > farshid.txt
this command listed all directories which have "farshid" in the path and save
it to the farshid.txt file
in python you can use below code to search and find specific folders and files
######################################################################################
import
import os
import glob
######################################################################################
config
root="C:\\\farshid\\\"
specific_directories=root+"/**/farshid/**/*.jpg"
path_dir_detection_check=""
######################################################################################
function
files= glob.glob(specific_directories, recursive=True)
for file in files:
b=file.rfind("farshid")
path_dir_detection=file[0:b-1]
if (path_dir_detection != path_dir_detection_check):
dirname = os.path.dirname(file)
print("******************************************************* Next
Directories ************************")
print(dirname)
path_dir_detection_check=path_dir_detection
print(file)
The source code can be found in
[GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr)
make -j$(sysctl -n hw.physicalcpu)
shift+enter -> run selection
menue
code-> pereferences -> user snippets -> python.json
pip freeze > requirements.txt
extensions
Visual Studio IntelliCode
SSH FS
ext install Kelvin.vscode-sshfs
command+ shift+ p ->SSH SF: Create new SSH SF configuration
code ~/.zshrc
Bracket Pair Colorizer 2 => color (){}[] different color
Prettier - Code formatter => when you save. setting->(format on save)
indent-rainbow
shell => .code
Compare Folders
Command +p = all files -> if I press alt+ it open new tab the file
control + ` = open terminal
Command + o= open folder
Command + , = open setting
Command + /= #
shift+enter=run one line of code
option+shift+arrow down = duplicate line in code
command+ click mouse => go to function
command++ -> bigger
command+ shift +P
command+ K , command+ S => shourcuts
command+ L => select currnt line
command+ left/right arrow => start or end of line
command+ P => go to file in search
git config --global core.excludesfile ~/.gitignore
code ~/.gitignore
brew install pyenv
brew install poetry
pyenv install 3.7.5
pyenv global 3.7.5
poetry new "name of project"
-> go to folder
-> change python version if you want in the pyproject.toml
pyenv global 3.7.5
poetry new "pytorch_pretrained"
poetry install
pip install --upgrade pip
poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv-
python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil
seldon_core spacy sklearn torch torchvision jupyter pycocotools cython
pyyaml==5.1
poetry remove torch torchvision
pip install --pre torch torchvision -f[
](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)
pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0
opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python-
dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools
cython
pip3 install pyyaml==5.1
pip3 install 'git+https://github.com/facebookresearch/detectron2.git'
pip3 install -U
'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
poetry shell
jupyter notebook
pyenv
pyenv install
pyenv install 3.7.5
cd folder
pyenv global 3.7.5
pyenv versions
python -> you can see this environments
poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc
poetry run which python
poetry run jupyter lab
pipenv install requests
pyenv virtualenvs cv-endpoint
pyenv activate cv-endpoint
====================
black python
[https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1)
The Uncompromising Code Formatter
pip install black
====================
pre-commit A framework for managing and maintaining multi-language pre-commit
hooks.
pip install pre-commit
brew install pre-commit
.pre-commit-config.yaml
repos:
\- repo:
[https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R)
rev: v1.8.0
hooks:
\- id: reorder-python-imports
exclude: notebooks/
language_version: python3.7
\- repo:
[https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto)
rev: 19.10b0
hooks:
\- id: black
exclude: notebooks/
language_version: python3.7
\- repo: [https://github.com/pre-commit/pre-commit-
hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre-
commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi)
rev: v2.4.0
hooks:
\- id: flake8
args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max-
complexity=15', '\--select=B,C,E,F,W,T4,B9']
exclude: notebooks/
language_version: python3.7
pre-commit install
pre-commit run --all-files
git
make file
make check
code .
**add path**
import sys
sys.path.append(r'C: )
**create Mat**
bin_im = np.zeros((5,16))
bin_im = bin_im.astype(np.uint8)*255
**contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n',
header='', footer='', comments='# ')
**DLL**
import ctypes
my_dll = r"C:\fffffff.dll"
lib = ctypes.windll.LoadLibrary(my_dll)
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
**read matlab mat file to python**
from scipy import io
res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False,
squeeze_me=True)
for k in res.keys():
print(k, res[k])
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision,
Deep Learning, Artificial superintelligence (ASI)](/home)
# Python
##
##
global _
folder, file name, functions,
const,
##
doc
read_all_image_in_folder
recursive = True
##
code
##
doc
manual progress bar for python based on number of images process.
##
code
##
doc
this code shows information about image
##
code
import unittest
for the functions: for example one time create file and then detected in
between all operations in this function
def setUp(self):
def tearDown(self)
for the class:
@classmethod
def setUpClass(cls):
@classmethod
def tearDownClass(cls):
#
List files and folders
If you want to list directories which shows specific folder name in path in
windows you can use
dir /s /b /o:n /ad "farshid" > farshid.txt
this command listed all directories which have "farshid" in the path and save
it to the farshid.txt file
in python you can use below code to search and find specific folders and files
######################################################################################
import
import os
import glob
######################################################################################
config
root="C:\\\farshid\\\"
specific_directories=root+"/**/farshid/**/*.jpg"
path_dir_detection_check=""
######################################################################################
function
files= glob.glob(specific_directories, recursive=True)
for file in files:
b=file.rfind("farshid")
path_dir_detection=file[0:b-1]
if (path_dir_detection != path_dir_detection_check):
dirname = os.path.dirname(file)
print("******************************************************* Next
Directories ************************")
print(dirname)
path_dir_detection_check=path_dir_detection
print(file)
The source code can be found in
[GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr)
make -j$(sysctl -n hw.physicalcpu)
shift+enter -> run selection
menue
code-> pereferences -> user snippets -> python.json
pip freeze > requirements.txt
extensions
Visual Studio IntelliCode
SSH FS
ext install Kelvin.vscode-sshfs
command+ shift+ p ->SSH SF: Create new SSH SF configuration
code ~/.zshrc
Bracket Pair Colorizer 2 => color (){}[] different color
Prettier - Code formatter => when you save. setting->(format on save)
indent-rainbow
shell => .code
Compare Folders
Command +p = all files -> if I press alt+ it open new tab the file
control + ` = open terminal
Command + o= open folder
Command + , = open setting
Command + /= #
shift+enter=run one line of code
option+shift+arrow down = duplicate line in code
command+ click mouse => go to function
command++ -> bigger
command+ shift +P
command+ K , command+ S => shourcuts
command+ L => select currnt line
command+ left/right arrow => start or end of line
command+ P => go to file in search
git config --global core.excludesfile ~/.gitignore
code ~/.gitignore
brew install pyenv
brew install poetry
pyenv install 3.7.5
pyenv global 3.7.5
poetry new "name of project"
-> go to folder
-> change python version if you want in the pyproject.toml
pyenv global 3.7.5
poetry new "pytorch_pretrained"
poetry install
pip install --upgrade pip
poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv-
python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil
seldon_core spacy sklearn torch torchvision jupyter pycocotools cython
pyyaml==5.1
poetry remove torch torchvision
pip install --pre torch torchvision -f[
](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)
pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0
opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python-
dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools
cython
pip3 install pyyaml==5.1
pip3 install 'git+https://github.com/facebookresearch/detectron2.git'
pip3 install -U
'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
poetry shell
jupyter notebook
pyenv
pyenv install
pyenv install 3.7.5
cd folder
pyenv global 3.7.5
pyenv versions
python -> you can see this environments
poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc
poetry run which python
poetry run jupyter lab
pipenv install requests
pyenv virtualenvs cv-endpoint
pyenv activate cv-endpoint
====================
black python
[https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1)
The Uncompromising Code Formatter
pip install black
====================
pre-commit A framework for managing and maintaining multi-language pre-commit
hooks.
pip install pre-commit
brew install pre-commit
.pre-commit-config.yaml
repos:
\- repo:
[https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R)
rev: v1.8.0
hooks:
\- id: reorder-python-imports
exclude: notebooks/
language_version: python3.7
\- repo:
[https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto)
rev: 19.10b0
hooks:
\- id: black
exclude: notebooks/
language_version: python3.7
\- repo: [https://github.com/pre-commit/pre-commit-
hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre-
commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi)
rev: v2.4.0
hooks:
\- id: flake8
args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max-
complexity=15', '\--select=B,C,E,F,W,T4,B9']
exclude: notebooks/
language_version: python3.7
pre-commit install
pre-commit run --all-files
git
make file
make check
code .
**add path**
import sys
sys.path.append(r'C: )
**create Mat**
bin_im = np.zeros((5,16))
bin_im = bin_im.astype(np.uint8)*255
**contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n',
header='', footer='', comments='# ')
**DLL**
import ctypes
my_dll = r"C:\fffffff.dll"
lib = ctypes.windll.LoadLibrary(my_dll)
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
**read matlab mat file to python**
from scipy import io
res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False,
squeeze_me=True)
for k in res.keys():
print(k, res[k])
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
[](/home)[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
* [Home](/home)
* [Product](/home/product)
* [Courses](/courses)
* [Machine Learning Specialization](/courses/machine-learning-specialization)
* [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach)
* [FSDL](/courses/fsdl)
* [Full Stack Deep Learning](/courses/full-stack-deep-learning)
* [MLOps](/courses/mlops)
* [ROS](/courses/ros)
* [Parallel Programming ](/courses/parallel-programming)
* [Modern CPP](/courses/modern-cpp)
* [Cloud-Native](/courses/cloud-native)
* [IoT Scholarship Foundation](/courses/iot-scholarship-foundation)
* [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization)
* [Workshops and Events](/workshops-and-events)
* [RISC-V](/workshops-and-events/risc-v)
* [Edge-AI-summit](/workshops-and-events/edge-ai-summit)
* [Embedded IoT](/workshops-and-events/embedded-iot)
* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
* [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning)
* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
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* [startup](/topics-and-projects/startup)
* [Links](/links)
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* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# Python
##
##
global _
folder, file name, functions,
const,
##
doc
read_all_image_in_folder
recursive = True
##
code
##
doc
manual progress bar for python based on number of images process.
##
code
##
doc
this code shows information about image
##
code
import unittest
for the functions: for example one time create file and then detected in
between all operations in this function
def setUp(self):
def tearDown(self)
for the class:
@classmethod
def setUpClass(cls):
@classmethod
def tearDownClass(cls):
#
List files and folders
If you want to list directories which shows specific folder name in path in
windows you can use
dir /s /b /o:n /ad "farshid" > farshid.txt
this command listed all directories which have "farshid" in the path and save
it to the farshid.txt file
in python you can use below code to search and find specific folders and files
######################################################################################
import
import os
import glob
######################################################################################
config
root="C:\\\farshid\\\"
specific_directories=root+"/**/farshid/**/*.jpg"
path_dir_detection_check=""
######################################################################################
function
files= glob.glob(specific_directories, recursive=True)
for file in files:
b=file.rfind("farshid")
path_dir_detection=file[0:b-1]
if (path_dir_detection != path_dir_detection_check):
dirname = os.path.dirname(file)
print("******************************************************* Next
Directories ************************")
print(dirname)
path_dir_detection_check=path_dir_detection
print(file)
The source code can be found in
[GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr)
make -j$(sysctl -n hw.physicalcpu)
shift+enter -> run selection
menue
code-> pereferences -> user snippets -> python.json
pip freeze > requirements.txt
extensions
Visual Studio IntelliCode
SSH FS
ext install Kelvin.vscode-sshfs
command+ shift+ p ->SSH SF: Create new SSH SF configuration
code ~/.zshrc
Bracket Pair Colorizer 2 => color (){}[] different color
Prettier - Code formatter => when you save. setting->(format on save)
indent-rainbow
shell => .code
Compare Folders
Command +p = all files -> if I press alt+ it open new tab the file
control + ` = open terminal
Command + o= open folder
Command + , = open setting
Command + /= #
shift+enter=run one line of code
option+shift+arrow down = duplicate line in code
command+ click mouse => go to function
command++ -> bigger
command+ shift +P
command+ K , command+ S => shourcuts
command+ L => select currnt line
command+ left/right arrow => start or end of line
command+ P => go to file in search
git config --global core.excludesfile ~/.gitignore
code ~/.gitignore
brew install pyenv
brew install poetry
pyenv install 3.7.5
pyenv global 3.7.5
poetry new "name of project"
-> go to folder
-> change python version if you want in the pyproject.toml
pyenv global 3.7.5
poetry new "pytorch_pretrained"
poetry install
pip install --upgrade pip
poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv-
python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil
seldon_core spacy sklearn torch torchvision jupyter pycocotools cython
pyyaml==5.1
poetry remove torch torchvision
pip install --pre torch torchvision -f[
](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)
pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0
opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python-
dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools
cython
pip3 install pyyaml==5.1
pip3 install 'git+https://github.com/facebookresearch/detectron2.git'
pip3 install -U
'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
poetry shell
jupyter notebook
pyenv
pyenv install
pyenv install 3.7.5
cd folder
pyenv global 3.7.5
pyenv versions
python -> you can see this environments
poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc
poetry run which python
poetry run jupyter lab
pipenv install requests
pyenv virtualenvs cv-endpoint
pyenv activate cv-endpoint
====================
black python
[https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1)
The Uncompromising Code Formatter
pip install black
====================
pre-commit A framework for managing and maintaining multi-language pre-commit
hooks.
pip install pre-commit
brew install pre-commit
.pre-commit-config.yaml
repos:
\- repo:
[https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R)
rev: v1.8.0
hooks:
\- id: reorder-python-imports
exclude: notebooks/
language_version: python3.7
\- repo:
[https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto)
rev: 19.10b0
hooks:
\- id: black
exclude: notebooks/
language_version: python3.7
\- repo: [https://github.com/pre-commit/pre-commit-
hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre-
commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi)
rev: v2.4.0
hooks:
\- id: flake8
args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max-
complexity=15', '\--select=B,C,E,F,W,T4,B9']
exclude: notebooks/
language_version: python3.7
pre-commit install
pre-commit run --all-files
git
make file
make check
code .
**add path**
import sys
sys.path.append(r'C: )
**create Mat**
bin_im = np.zeros((5,16))
bin_im = bin_im.astype(np.uint8)*255
**contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n',
header='', footer='', comments='# ')
**DLL**
import ctypes
my_dll = r"C:\fffffff.dll"
lib = ctypes.windll.LoadLibrary(my_dll)
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
**read matlab mat file to python**
from scipy import io
res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False,
squeeze_me=True)
for k in res.keys():
print(k, res[k])
**remove background or minimum form image**
im = im - im.min()
**time**
e1 = cv2.getTickCount()
######
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
from scipy.signal import **find_peaks**
peaks, out = find_peaks(Nf, distance=25)
if peaks[0] < 25:
peaks = peaks[1:]
heights = Nf[peaks]
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
import sys
sys.path.append(r'C: )
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* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# C++
#
Clean Code for Computer Vision using OpenCV and C++
When writing clean code using the OpenCV library in C++, here are some
additional principles to follow:
1. Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning.
2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations.
3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does.
4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task.
5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately.
6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code.
7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results.
These examples demonstrate how following good coding practices and paying
attention to the specific features of the OpenCV library can help you write
clean, efficient, and effective code.
By following these principles, you can write clean and maintainable code that
makes effective use of the OpenCV library.
Here are several examples of clean code in OpenCV C++:
* Meaningful variable names:
cv::Mat original_image = cv::imread("image.jpg");
cv::Mat resized_image;
cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5,
cv::INTER_AREA);
* Use of high-level functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat dst;
cv::GaussianBlur(src, dst, cv::Size(3,3), 0);
* Error handling:
cv::Mat src = cv::imread("image.jpg");
if(src.empty()) {
std::cout << "Error: Could not load image" << std::endl;
return -1;
}
* Use of descriptive function names:
cv::Mat src
* Appropriate use of OpenCV data structures:
cv::Mat src = cv::imread("image.jpg");
std::vector corners;
cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10);
* Reusable functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
cv::Mat sharpen_image(const cv::Mat& image) {
cv::Mat result;
cv::GaussianBlur(image, result, cv::Size(0,0), 3);
cv::addWeighted(image, 1.5, result, -0.5, 0, result);
return result;
}
cv::Mat sharpened = sharpen_image(gray);
* Clear and concise comments:
// Load the source image
cv::Mat src = cv::imread("image.jpg");
// Convert the image to grayscale
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
// Threshold the image to create a binary image
cv::Mat thresholded;
cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY);
##
doc
this code shows information about image
##
code
##
doc
this code shows information about image
##
code
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traffic. Information about your use of this site is shared with Google. By
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* [Tesla](/workshops-and-events/tesla)
* [AI-Hardware](/workshops-and-events/ai-hardware)
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* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
* [ChatGPT](/topics-and-projects/chatgpt)
* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# C++
#
Clean Code for Computer Vision using OpenCV and C++
When writing clean code using the OpenCV library in C++, here are some
additional principles to follow:
1. Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning.
2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations.
3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does.
4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task.
5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately.
6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code.
7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results.
These examples demonstrate how following good coding practices and paying
attention to the specific features of the OpenCV library can help you write
clean, efficient, and effective code.
By following these principles, you can write clean and maintainable code that
makes effective use of the OpenCV library.
Here are several examples of clean code in OpenCV C++:
* Meaningful variable names:
cv::Mat original_image = cv::imread("image.jpg");
cv::Mat resized_image;
cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5,
cv::INTER_AREA);
* Use of high-level functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat dst;
cv::GaussianBlur(src, dst, cv::Size(3,3), 0);
* Error handling:
cv::Mat src = cv::imread("image.jpg");
if(src.empty()) {
std::cout << "Error: Could not load image" << std::endl;
return -1;
}
* Use of descriptive function names:
cv::Mat src
* Appropriate use of OpenCV data structures:
cv::Mat src = cv::imread("image.jpg");
std::vector corners;
cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10);
* Reusable functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
cv::Mat sharpen_image(const cv::Mat& image) {
cv::Mat result;
cv::GaussianBlur(image, result, cv::Size(0,0), 3);
cv::addWeighted(image, 1.5, result, -0.5, 0, result);
return result;
}
cv::Mat sharpened = sharpen_image(gray);
* Clear and concise comments:
// Load the source image
cv::Mat src = cv::imread("image.jpg");
// Convert the image to grayscale
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
// Threshold the image to create a binary image
cv::Mat thresholded;
cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY);
##
doc
this code shows information about image
##
code
##
doc
this code shows information about image
##
code
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* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
* [IFA2022](/workshops-and-events/ifa2022)
* [Book Summary ](/book-summary)
* [commonplace book](/book-summary/commonplace-book)
* [knowledge_management](/book-summary/knowledge_management)
* [PKM](/book-summary/knowledge_management/pkm)
* [Topics and Projects](/topics-and-projects)
* [AI_Hub](/topics-and-projects/ai_hub)
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* [How to start](/topics-and-projects/how-to-start)
* [YouTube](/topics-and-projects/how-to-start/youtube)
* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# C++
#
Clean Code for Computer Vision using OpenCV and C++
When writing clean code using the OpenCV library in C++, here are some
additional principles to follow:
1. Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning.
2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations.
3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does.
4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task.
5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately.
6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code.
7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results.
These examples demonstrate how following good coding practices and paying
attention to the specific features of the OpenCV library can help you write
clean, efficient, and effective code.
By following these principles, you can write clean and maintainable code that
makes effective use of the OpenCV library.
Here are several examples of clean code in OpenCV C++:
* Meaningful variable names:
cv::Mat original_image = cv::imread("image.jpg");
cv::Mat resized_image;
cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5,
cv::INTER_AREA);
* Use of high-level functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat dst;
cv::GaussianBlur(src, dst, cv::Size(3,3), 0);
* Error handling:
cv::Mat src = cv::imread("image.jpg");
if(src.empty()) {
std::cout << "Error: Could not load image" << std::endl;
return -1;
}
* Use of descriptive function names:
cv::Mat src
* Appropriate use of OpenCV data structures:
cv::Mat src = cv::imread("image.jpg");
std::vector corners;
cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10);
* Reusable functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
cv::Mat sharpen_image(const cv::Mat& image) {
cv::Mat result;
cv::GaussianBlur(image, result, cv::Size(0,0), 3);
cv::addWeighted(image, 1.5, result, -0.5, 0, result);
return result;
}
cv::Mat sharpened = sharpen_image(gray);
* Clear and concise comments:
// Load the source image
cv::Mat src = cv::imread("image.jpg");
// Convert the image to grayscale
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
// Threshold the image to create a binary image
cv::Mat thresholded;
cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY);
##
doc
this code shows information about image
##
code
##
doc
this code shows information about image
##
code
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* [Metaverse](/workshops-and-events/metaverse)
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* [Book Summary ](/book-summary)
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* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# C++
#
Clean Code for Computer Vision using OpenCV and C++
When writing clean code using the OpenCV library in C++, here are some
additional principles to follow:
1. Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning.
2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations.
3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does.
4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task.
5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately.
6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code.
7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results.
These examples demonstrate how following good coding practices and paying
attention to the specific features of the OpenCV library can help you write
clean, efficient, and effective code.
By following these principles, you can write clean and maintainable code that
makes effective use of the OpenCV library.
Here are several examples of clean code in OpenCV C++:
* Meaningful variable names:
cv::Mat original_image = cv::imread("image.jpg");
cv::Mat resized_image;
cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5,
cv::INTER_AREA);
* Use of high-level functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat dst;
cv::GaussianBlur(src, dst, cv::Size(3,3), 0);
* Error handling:
cv::Mat src = cv::imread("image.jpg");
if(src.empty()) {
std::cout << "Error: Could not load image" << std::endl;
return -1;
}
* Use of descriptive function names:
cv::Mat src
* Appropriate use of OpenCV data structures:
cv::Mat src = cv::imread("image.jpg");
std::vector corners;
cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10);
* Reusable functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
cv::Mat sharpen_image(const cv::Mat& image) {
cv::Mat result;
cv::GaussianBlur(image, result, cv::Size(0,0), 3);
cv::addWeighted(image, 1.5, result, -0.5, 0, result);
return result;
}
cv::Mat sharpened = sharpen_image(gray);
* Clear and concise comments:
// Load the source image
cv::Mat src = cv::imread("image.jpg");
// Convert the image to grayscale
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
// Threshold the image to create a binary image
cv::Mat thresholded;
cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY);
##
doc
this code shows information about image
##
code
##
doc
this code shows information about image
##
code
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
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[](/home)[Computer
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* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer
Vision, Deep Learning, Artificial superintelligence (ASI)](/home)
# C++
#
Clean Code for Computer Vision using OpenCV and C++
When writing clean code using the OpenCV library in C++, here are some
additional principles to follow:
1. Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning.
2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations.
3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does.
4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task.
5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately.
6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code.
7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results.
These examples demonstrate how following good coding practices and paying
attention to the specific features of the OpenCV library can help you write
clean, efficient, and effective code.
By following these principles, you can write clean and maintainable code that
makes effective use of the OpenCV library.
Here are several examples of clean code in OpenCV C++:
* Meaningful variable names:
cv::Mat original_image = cv::imread("image.jpg");
cv::Mat resized_image;
cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5,
cv::INTER_AREA);
* Use of high-level functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat dst;
cv::GaussianBlur(src, dst, cv::Size(3,3), 0);
* Error handling:
cv::Mat src = cv::imread("image.jpg");
if(src.empty()) {
std::cout << "Error: Could not load image" << std::endl;
return -1;
}
* Use of descriptive function names:
cv::Mat src
* Appropriate use of OpenCV data structures:
cv::Mat src = cv::imread("image.jpg");
std::vector corners;
cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10);
* Reusable functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
cv::Mat sharpen_image(const cv::Mat& image) {
cv::Mat result;
cv::GaussianBlur(image, result, cv::Size(0,0), 3);
cv::addWeighted(image, 1.5, result, -0.5, 0, result);
return result;
}
cv::Mat sharpened = sharpen_image(gray);
* Clear and concise comments:
// Load the source image
cv::Mat src = cv::imread("image.jpg");
// Convert the image to grayscale
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
// Threshold the image to create a binary image
cv::Mat thresholded;
cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY);
##
doc
this code shows information about image
##
code
##
doc
this code shows information about image
##
code
This site uses cookies from Google to deliver its services and to analyze
traffic. Information about your use of this site is shared with Google. By
using this site, you agree to its use of cookies.
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* [Metaverse](/workshops-and-events/metaverse)
* [Workshops](/workshops-and-events/workshops)
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* [Book Summary ](/book-summary)
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* [Topics and Projects](/topics-and-projects)
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* [How to start](/topics-and-projects/how-to-start)
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* [YouTube II](/topics-and-projects/how-to-start/youtube-ii)
* [Software](/topics-and-projects/how-to-start/software)
* [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing)
* [Source Code](/topics-and-projects/source-code)
* [OpenCV](/topics-and-projects/source-code/opencv)
* [CPP](/topics-and-projects/source-code/opencv/cpp)
* [python](/topics-and-projects/source-code/opencv/python)
* [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv)
* [Rust](/topics-and-projects/source-code/opencv/rust)
* [compile](/topics-and-projects/source-code/compile)
* [IoT](/topics-and-projects/source-code/iot)
* [Share](/topics-and-projects/share)
* [Video Tracking](/topics-and-projects/video-tracking)
* [Camera_Calibration](/topics-and-projects/camera_calibration)
* [DRL](/topics-and-projects/drl)
* [Hardware](/topics-and-projects/hardware)
* [Quantum Computing](/topics-and-projects/quantum-computing)
* [AltCoin](/topics-and-projects/altcoin)
* [Resume_CV](/topics-and-projects/resume_cv)
* [فارسی](/topics-and-projects/فارسی)
* [Apple](/topics-and-projects/apple)
* [startup](/topics-and-projects/startup)
* [Links](/links)
* [amazon](/links/amazon)
* [About](/about)
* [FQA](/about/fqa)
[Computer Vision, Deep
Learning, Artificial superintelligence (ASI)](/home)
# C++
#
Clean Code for Computer Vision using OpenCV and C++
When writing clean code using the OpenCV library in C++, here are some
additional principles to follow:
1. Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning.
2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations.
3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does.
4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task.
5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately.
6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code.
7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results.
These examples demonstrate how following good coding practices and paying
attention to the specific features of the OpenCV library can help you write
clean, efficient, and effective code.
By following these principles, you can write clean and maintainable code that
makes effective use of the OpenCV library.
Here are several examples of clean code in OpenCV C++:
* Meaningful variable names:
cv::Mat original_image = cv::imread("image.jpg");
cv::Mat resized_image;
cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5,
cv::INTER_AREA);
* Use of high-level functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat dst;
cv::GaussianBlur(src, dst, cv::Size(3,3), 0);
* Error handling:
cv::Mat src = cv::imread("image.jpg");
if(src.empty()) {
std::cout << "Error: Could not load image" << std::endl;
return -1;
}
* Use of descriptive function names:
cv::Mat src
* Appropriate use of OpenCV data structures:
cv::Mat src = cv::imread("image.jpg");
std::vector corners;
cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10);
* Reusable functions:
cv::Mat src = cv::imread("image.jpg");
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
cv::Mat sharpen_image(const cv::Mat& image) {
cv::Mat result;
cv::GaussianBlur(image, result, cv::Size(0,0), 3);
cv::addWeighted(image, 1.5, result, -0.5, 0, result);
return result;
}
cv::Mat sharpened = sharpen_image(gray);
* Clear and concise comments:
// Load the source image
cv::Mat src = cv::imread("image.jpg");
// Convert the image to grayscale
cv::Mat gray;
cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
// Threshold the image to create a binary image
cv::Mat thresholded;
cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY);
##
doc
this code shows information about image
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# OpenCV
Download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
# [C++](/topics-and-projects/source-code/opencv/cpp)
# [Python](/topics-and-projects/source-code/opencv/python)
NuGet - OpenCV 5 beta
cvtest: Computer Vision Test
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Standard test for computer vision application
Advanced OpenCV techniques:
Advanced OpenCV techniques:
Advanced OpenCV techniques: balance white
Advanced OpenCV techniques: contrast and brightness
Advanced subpixel techniques: Shift image content
Mesh grid float
mesh grid int
main:
Tips and Tricks of OpenCV that Nobody Told You
Tricks
Tips
Save results
Error
Testing for OpenCV Projects
Tricks
Tips
Example
YouTube
#
NuGet - OpenCV 5 beta
NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
#OpenCV #Farshid_PirahanSiah #pirahansiah
My NuGet packages comprised of two versions for different VS version.
* Visual Studio 2019
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9)
* Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7
* Visual Studio 2022
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl)
* Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7
_**more:**_[ _
**https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
_ ****_
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present
"Open Presentation, cvtest in new window")
cvtest
#
Advanced OpenCV techniques:
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
#
Advanced OpenCV techniques:
Cross correlation (CC): TM_CCORR
Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF
Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED
maximum absolute difference metric (MaxAD), which is also known as the uniform
distance metric computeECC() and findTransformECC().
Sum of absolute differences (SAD)
Cross correlation (CC)
find identical regions of an image that match a template, select by giving a
threshold
2D convolution
It simply slides the template image over the input image (as in 2D
convolution) and compares the template and patch of input image under the
template image.
Template matching
> cv2.TM_CCOEFF
> cv2.TM_CCOEFF_NORMED
> cv2.TM_CCORR
> cv2.TM_CCORR_NORMED
< cv2.TM_SQDIFF
< cv2.TM_SQDIFF_NORMED
cv2.minMaxLoc()
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM)
[https://stackoverflow.com/questions/58158129/understanding-and-evaluating-
template-matching-
methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding-
and-evaluating-template-matching-
methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY)
#
Advanced OpenCV techniques: balance white
balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
void balance_white(cv::Mat mat) {
double discard_ratio = 0.05;
int hists[3][256];
memset(hists, 0, 3 * 256 * sizeof(int));
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
hists[j][ptr[x * 3 + j]] += 1;
}
}
}
// cumulative hist
int total = mat.cols * mat.rows;
int vmin[3], vmax[3];
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 255; ++j) {
hists[i][j + 1] += hists[i][j];
}
vmin[i] = 0;
vmax[i] = 255;
while (hists[i][vmin[i]] < discard_ratio * total)
vmin[i] += 1;
while (hists[i][vmax[i]] > (1 - discard_ratio) * total)
vmax[i] -= 1;
if (vmax[i] < 255 - 1)
vmax[i] += 1;
}
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
int val = ptr[x * 3 + j];
if (val < vmin[j])
val = vmin[j];
if (val > vmax[j])
val = vmax[j];
ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] -
vmin[j]));
}
}
}
}
reference http://www.ipol.im/pub/art/2011/llmps-scb/
https://gist.github.com/tomykaira/94472e9f4921ec2cf582
#
Advanced OpenCV techniques: contrast and brightness
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
showImage.convertTo(showImage, CV_32FC3);
double alpha = 2.0; /*< Simple contrast control */
int beta = 100; /*< Simple brightness control */
for (int y = 0; y < showImage.rows; y++) {
for (int x = 0; x < showImage.cols; x++) {
for (int c = 0; c < showImage.channels(); c++) {
showImage.at(y, x)[c] =cv::saturate_cast(alpha *
showImage.at(y, x)[c] + beta);
}
}
}
showImage.convertTo(showImage, CV_8UC3);
cv::imshow("Changing the contrast and brightness of an image! ", showImage);
cv::waitKey(0);
based on
[https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB)
[https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7)
#
Advanced subpixel techniques: Shift image content
sub-pixel, floating points, mesh grid, remap, more precise, real-valued
coordinates, moving image pixel, Shift image content with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
##
##
Mesh grid float
static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
mesh grid int
static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
main:
cv::Mat1f XF, YF;
//for int
//meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF,
YF);
//for float
meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF,
YF);
for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) {
for (int colsImage = 0; colsImage < cols_main; ++colsImage) {
XF.at(rowsImage, colsImage) += offset1;
YF.at(rowsImage, colsImage) += offset2;
}
}
cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR);
if (show)
{
cv::Mat resizedImage = dst.clone();
dst.convertTo(resizedImage, CV_8UC3);
cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5);
std::string nameWindow = " meshgrid and remap in float ";
cv::imshow(nameWindow, resizedImage);
cv::waitKey(0);
}
#
Tips and Tricks of OpenCV that Nobody Told You
Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
###
Tricks
cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color
cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale
//copy small Mat to bigger Mat
cv::Rect roi( cv::Point( originX, originY ), smallImage.size() );
smallImage.copyTo( bigImage( roi ) );
###
Tips
* copy mat to vector need clone()
###
###
Save results
* save image in float
* cv::imwrite("image.exr", MatImage);
* save image in uncompressed format :
* cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 });
* * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate
* cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save images in streaming
* int64 t0 = cv::getTickCount();
* std::string fileName= "fashid_"+std::to_string(t0)+ ".png";
* cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save file name:
* std::filesystem::path p = std::filesystem::path(files[i]).filename();
* std::string imgFile = savePath + "/" \+ p.string() + ".tiff";
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ;
###
Error
* Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000.
* check the size of Mat
* cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F);
* cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F);
* Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease'
* the link files are not match based on release or debug mode.
#
Testing for OpenCV Projects
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
* Arrange, Act and Assert (AAA) Pattern
* Google C++ Test Framework
* Assertions Types and Test Fixtures
*
ASSERT_FALSE(frame.empty());
ASSERT_NO_THROW(cap >> img);
ASSERT_FALSE(img.empty()) << "idx=" << idx;
###
Tricks
embedded system, keep the software as small as possible, Embedding static
elements in your application,
[https://gstreamer.freedesktop.org/documentation/installing/index.html?gi-
language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi-
language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y)
###
Tips
* copy mat to vector need clone()
###
###
Example
#include
assert(!im.empty());
assert(x.size()==y.size());
assert(x.size()>2);
#ifdef _DEBUG
#endif
#if true
#else
#endif
#
YouTube
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
[https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u)
A set of C++ APIs are provided to mimic the same behaviors as the MATLAB
function "linspace" and "meshgrid".
Must-Read Books of All Time in Computer Vision and Machine Learning
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# OpenCV
Download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
# [C++](/topics-and-projects/source-code/opencv/cpp)
# [Python](/topics-and-projects/source-code/opencv/python)
NuGet - OpenCV 5 beta
cvtest: Computer Vision Test
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Standard test for computer vision application
Advanced OpenCV techniques:
Advanced OpenCV techniques:
Advanced OpenCV techniques: balance white
Advanced OpenCV techniques: contrast and brightness
Advanced subpixel techniques: Shift image content
Mesh grid float
mesh grid int
main:
Tips and Tricks of OpenCV that Nobody Told You
Tricks
Tips
Save results
Error
Testing for OpenCV Projects
Tricks
Tips
Example
YouTube
#
NuGet - OpenCV 5 beta
NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
#OpenCV #Farshid_PirahanSiah #pirahansiah
My NuGet packages comprised of two versions for different VS version.
* Visual Studio 2019
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9)
* Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7
* Visual Studio 2022
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl)
* Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7
_**more:**_[ _
**https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
_ ****_
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present
"Open Presentation, cvtest in new window")
cvtest
#
Advanced OpenCV techniques:
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
#
Advanced OpenCV techniques:
Cross correlation (CC): TM_CCORR
Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF
Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED
maximum absolute difference metric (MaxAD), which is also known as the uniform
distance metric computeECC() and findTransformECC().
Sum of absolute differences (SAD)
Cross correlation (CC)
find identical regions of an image that match a template, select by giving a
threshold
2D convolution
It simply slides the template image over the input image (as in 2D
convolution) and compares the template and patch of input image under the
template image.
Template matching
> cv2.TM_CCOEFF
> cv2.TM_CCOEFF_NORMED
> cv2.TM_CCORR
> cv2.TM_CCORR_NORMED
< cv2.TM_SQDIFF
< cv2.TM_SQDIFF_NORMED
cv2.minMaxLoc()
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM)
[https://stackoverflow.com/questions/58158129/understanding-and-evaluating-
template-matching-
methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding-
and-evaluating-template-matching-
methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY)
#
Advanced OpenCV techniques: balance white
balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
void balance_white(cv::Mat mat) {
double discard_ratio = 0.05;
int hists[3][256];
memset(hists, 0, 3 * 256 * sizeof(int));
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
hists[j][ptr[x * 3 + j]] += 1;
}
}
}
// cumulative hist
int total = mat.cols * mat.rows;
int vmin[3], vmax[3];
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 255; ++j) {
hists[i][j + 1] += hists[i][j];
}
vmin[i] = 0;
vmax[i] = 255;
while (hists[i][vmin[i]] < discard_ratio * total)
vmin[i] += 1;
while (hists[i][vmax[i]] > (1 - discard_ratio) * total)
vmax[i] -= 1;
if (vmax[i] < 255 - 1)
vmax[i] += 1;
}
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
int val = ptr[x * 3 + j];
if (val < vmin[j])
val = vmin[j];
if (val > vmax[j])
val = vmax[j];
ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] -
vmin[j]));
}
}
}
}
reference http://www.ipol.im/pub/art/2011/llmps-scb/
https://gist.github.com/tomykaira/94472e9f4921ec2cf582
#
Advanced OpenCV techniques: contrast and brightness
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
showImage.convertTo(showImage, CV_32FC3);
double alpha = 2.0; /*< Simple contrast control */
int beta = 100; /*< Simple brightness control */
for (int y = 0; y < showImage.rows; y++) {
for (int x = 0; x < showImage.cols; x++) {
for (int c = 0; c < showImage.channels(); c++) {
showImage.at(y, x)[c] =cv::saturate_cast(alpha *
showImage.at(y, x)[c] + beta);
}
}
}
showImage.convertTo(showImage, CV_8UC3);
cv::imshow("Changing the contrast and brightness of an image! ", showImage);
cv::waitKey(0);
based on
[https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB)
[https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7)
#
Advanced subpixel techniques: Shift image content
sub-pixel, floating points, mesh grid, remap, more precise, real-valued
coordinates, moving image pixel, Shift image content with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
##
##
Mesh grid float
static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
mesh grid int
static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
main:
cv::Mat1f XF, YF;
//for int
//meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF,
YF);
//for float
meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF,
YF);
for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) {
for (int colsImage = 0; colsImage < cols_main; ++colsImage) {
XF.at(rowsImage, colsImage) += offset1;
YF.at(rowsImage, colsImage) += offset2;
}
}
cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR);
if (show)
{
cv::Mat resizedImage = dst.clone();
dst.convertTo(resizedImage, CV_8UC3);
cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5);
std::string nameWindow = " meshgrid and remap in float ";
cv::imshow(nameWindow, resizedImage);
cv::waitKey(0);
}
#
Tips and Tricks of OpenCV that Nobody Told You
Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
###
Tricks
cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color
cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale
//copy small Mat to bigger Mat
cv::Rect roi( cv::Point( originX, originY ), smallImage.size() );
smallImage.copyTo( bigImage( roi ) );
###
Tips
* copy mat to vector need clone()
###
###
Save results
* save image in float
* cv::imwrite("image.exr", MatImage);
* save image in uncompressed format :
* cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 });
* * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate
* cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save images in streaming
* int64 t0 = cv::getTickCount();
* std::string fileName= "fashid_"+std::to_string(t0)+ ".png";
* cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save file name:
* std::filesystem::path p = std::filesystem::path(files[i]).filename();
* std::string imgFile = savePath + "/" \+ p.string() + ".tiff";
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ;
###
Error
* Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000.
* check the size of Mat
* cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F);
* cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F);
* Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease'
* the link files are not match based on release or debug mode.
#
Testing for OpenCV Projects
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
* Arrange, Act and Assert (AAA) Pattern
* Google C++ Test Framework
* Assertions Types and Test Fixtures
*
ASSERT_FALSE(frame.empty());
ASSERT_NO_THROW(cap >> img);
ASSERT_FALSE(img.empty()) << "idx=" << idx;
###
Tricks
embedded system, keep the software as small as possible, Embedding static
elements in your application,
[https://gstreamer.freedesktop.org/documentation/installing/index.html?gi-
language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi-
language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y)
###
Tips
* copy mat to vector need clone()
###
###
Example
#include
assert(!im.empty());
assert(x.size()==y.size());
assert(x.size()>2);
#ifdef _DEBUG
#endif
#if true
#else
#endif
#
YouTube
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
[https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u)
A set of C++ APIs are provided to mimic the same behaviors as the MATLAB
function "linspace" and "meshgrid".
Must-Read Books of All Time in Computer Vision and Machine Learning
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# OpenCV
Download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
# [C++](/topics-and-projects/source-code/opencv/cpp)
# [Python](/topics-and-projects/source-code/opencv/python)
NuGet - OpenCV 5 beta
cvtest: Computer Vision Test
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Standard test for computer vision application
Advanced OpenCV techniques:
Advanced OpenCV techniques:
Advanced OpenCV techniques: balance white
Advanced OpenCV techniques: contrast and brightness
Advanced subpixel techniques: Shift image content
Mesh grid float
mesh grid int
main:
Tips and Tricks of OpenCV that Nobody Told You
Tricks
Tips
Save results
Error
Testing for OpenCV Projects
Tricks
Tips
Example
YouTube
#
NuGet - OpenCV 5 beta
NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
#OpenCV #Farshid_PirahanSiah #pirahansiah
My NuGet packages comprised of two versions for different VS version.
* Visual Studio 2019
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9)
* Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7
* Visual Studio 2022
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl)
* Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7
_**more:**_[ _
**https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
_ ****_
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present
"Open Presentation, cvtest in new window")
cvtest
#
Advanced OpenCV techniques:
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
#
Advanced OpenCV techniques:
Cross correlation (CC): TM_CCORR
Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF
Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED
maximum absolute difference metric (MaxAD), which is also known as the uniform
distance metric computeECC() and findTransformECC().
Sum of absolute differences (SAD)
Cross correlation (CC)
find identical regions of an image that match a template, select by giving a
threshold
2D convolution
It simply slides the template image over the input image (as in 2D
convolution) and compares the template and patch of input image under the
template image.
Template matching
> cv2.TM_CCOEFF
> cv2.TM_CCOEFF_NORMED
> cv2.TM_CCORR
> cv2.TM_CCORR_NORMED
< cv2.TM_SQDIFF
< cv2.TM_SQDIFF_NORMED
cv2.minMaxLoc()
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM)
[https://stackoverflow.com/questions/58158129/understanding-and-evaluating-
template-matching-
methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding-
and-evaluating-template-matching-
methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY)
#
Advanced OpenCV techniques: balance white
balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
void balance_white(cv::Mat mat) {
double discard_ratio = 0.05;
int hists[3][256];
memset(hists, 0, 3 * 256 * sizeof(int));
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
hists[j][ptr[x * 3 + j]] += 1;
}
}
}
// cumulative hist
int total = mat.cols * mat.rows;
int vmin[3], vmax[3];
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 255; ++j) {
hists[i][j + 1] += hists[i][j];
}
vmin[i] = 0;
vmax[i] = 255;
while (hists[i][vmin[i]] < discard_ratio * total)
vmin[i] += 1;
while (hists[i][vmax[i]] > (1 - discard_ratio) * total)
vmax[i] -= 1;
if (vmax[i] < 255 - 1)
vmax[i] += 1;
}
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
int val = ptr[x * 3 + j];
if (val < vmin[j])
val = vmin[j];
if (val > vmax[j])
val = vmax[j];
ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] -
vmin[j]));
}
}
}
}
reference http://www.ipol.im/pub/art/2011/llmps-scb/
https://gist.github.com/tomykaira/94472e9f4921ec2cf582
#
Advanced OpenCV techniques: contrast and brightness
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
showImage.convertTo(showImage, CV_32FC3);
double alpha = 2.0; /*< Simple contrast control */
int beta = 100; /*< Simple brightness control */
for (int y = 0; y < showImage.rows; y++) {
for (int x = 0; x < showImage.cols; x++) {
for (int c = 0; c < showImage.channels(); c++) {
showImage.at(y, x)[c] =cv::saturate_cast(alpha *
showImage.at(y, x)[c] + beta);
}
}
}
showImage.convertTo(showImage, CV_8UC3);
cv::imshow("Changing the contrast and brightness of an image! ", showImage);
cv::waitKey(0);
based on
[https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB)
[https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7)
#
Advanced subpixel techniques: Shift image content
sub-pixel, floating points, mesh grid, remap, more precise, real-valued
coordinates, moving image pixel, Shift image content with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
##
##
Mesh grid float
static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
mesh grid int
static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
main:
cv::Mat1f XF, YF;
//for int
//meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF,
YF);
//for float
meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF,
YF);
for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) {
for (int colsImage = 0; colsImage < cols_main; ++colsImage) {
XF.at(rowsImage, colsImage) += offset1;
YF.at(rowsImage, colsImage) += offset2;
}
}
cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR);
if (show)
{
cv::Mat resizedImage = dst.clone();
dst.convertTo(resizedImage, CV_8UC3);
cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5);
std::string nameWindow = " meshgrid and remap in float ";
cv::imshow(nameWindow, resizedImage);
cv::waitKey(0);
}
#
Tips and Tricks of OpenCV that Nobody Told You
Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
###
Tricks
cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color
cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale
//copy small Mat to bigger Mat
cv::Rect roi( cv::Point( originX, originY ), smallImage.size() );
smallImage.copyTo( bigImage( roi ) );
###
Tips
* copy mat to vector need clone()
###
###
Save results
* save image in float
* cv::imwrite("image.exr", MatImage);
* save image in uncompressed format :
* cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 });
* * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate
* cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save images in streaming
* int64 t0 = cv::getTickCount();
* std::string fileName= "fashid_"+std::to_string(t0)+ ".png";
* cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save file name:
* std::filesystem::path p = std::filesystem::path(files[i]).filename();
* std::string imgFile = savePath + "/" \+ p.string() + ".tiff";
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ;
###
Error
* Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000.
* check the size of Mat
* cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F);
* cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F);
* Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease'
* the link files are not match based on release or debug mode.
#
Testing for OpenCV Projects
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
* Arrange, Act and Assert (AAA) Pattern
* Google C++ Test Framework
* Assertions Types and Test Fixtures
*
ASSERT_FALSE(frame.empty());
ASSERT_NO_THROW(cap >> img);
ASSERT_FALSE(img.empty()) << "idx=" << idx;
###
Tricks
embedded system, keep the software as small as possible, Embedding static
elements in your application,
[https://gstreamer.freedesktop.org/documentation/installing/index.html?gi-
language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi-
language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y)
###
Tips
* copy mat to vector need clone()
###
###
Example
#include
assert(!im.empty());
assert(x.size()==y.size());
assert(x.size()>2);
#ifdef _DEBUG
#endif
#if true
#else
#endif
#
YouTube
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
[https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u)
A set of C++ APIs are provided to mimic the same behaviors as the MATLAB
function "linspace" and "meshgrid".
Must-Read Books of All Time in Computer Vision and Machine Learning
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# OpenCV
Download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
# [C++](/topics-and-projects/source-code/opencv/cpp)
# [Python](/topics-and-projects/source-code/opencv/python)
NuGet - OpenCV 5 beta
cvtest: Computer Vision Test
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Standard test for computer vision application
Advanced OpenCV techniques:
Advanced OpenCV techniques:
Advanced OpenCV techniques: balance white
Advanced OpenCV techniques: contrast and brightness
Advanced subpixel techniques: Shift image content
Mesh grid float
mesh grid int
main:
Tips and Tricks of OpenCV that Nobody Told You
Tricks
Tips
Save results
Error
Testing for OpenCV Projects
Tricks
Tips
Example
YouTube
#
NuGet - OpenCV 5 beta
NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
#OpenCV #Farshid_PirahanSiah #pirahansiah
My NuGet packages comprised of two versions for different VS version.
* Visual Studio 2019
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9)
* Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7
* Visual Studio 2022
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl)
* Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7
_**more:**_[ _
**https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
_ ****_
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present
"Open Presentation, cvtest in new window")
cvtest
#
Advanced OpenCV techniques:
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
#
Advanced OpenCV techniques:
Cross correlation (CC): TM_CCORR
Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF
Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED
maximum absolute difference metric (MaxAD), which is also known as the uniform
distance metric computeECC() and findTransformECC().
Sum of absolute differences (SAD)
Cross correlation (CC)
find identical regions of an image that match a template, select by giving a
threshold
2D convolution
It simply slides the template image over the input image (as in 2D
convolution) and compares the template and patch of input image under the
template image.
Template matching
> cv2.TM_CCOEFF
> cv2.TM_CCOEFF_NORMED
> cv2.TM_CCORR
> cv2.TM_CCORR_NORMED
< cv2.TM_SQDIFF
< cv2.TM_SQDIFF_NORMED
cv2.minMaxLoc()
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM)
[https://stackoverflow.com/questions/58158129/understanding-and-evaluating-
template-matching-
methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding-
and-evaluating-template-matching-
methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY)
#
Advanced OpenCV techniques: balance white
balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
void balance_white(cv::Mat mat) {
double discard_ratio = 0.05;
int hists[3][256];
memset(hists, 0, 3 * 256 * sizeof(int));
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
hists[j][ptr[x * 3 + j]] += 1;
}
}
}
// cumulative hist
int total = mat.cols * mat.rows;
int vmin[3], vmax[3];
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 255; ++j) {
hists[i][j + 1] += hists[i][j];
}
vmin[i] = 0;
vmax[i] = 255;
while (hists[i][vmin[i]] < discard_ratio * total)
vmin[i] += 1;
while (hists[i][vmax[i]] > (1 - discard_ratio) * total)
vmax[i] -= 1;
if (vmax[i] < 255 - 1)
vmax[i] += 1;
}
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
int val = ptr[x * 3 + j];
if (val < vmin[j])
val = vmin[j];
if (val > vmax[j])
val = vmax[j];
ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] -
vmin[j]));
}
}
}
}
reference http://www.ipol.im/pub/art/2011/llmps-scb/
https://gist.github.com/tomykaira/94472e9f4921ec2cf582
#
Advanced OpenCV techniques: contrast and brightness
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
showImage.convertTo(showImage, CV_32FC3);
double alpha = 2.0; /*< Simple contrast control */
int beta = 100; /*< Simple brightness control */
for (int y = 0; y < showImage.rows; y++) {
for (int x = 0; x < showImage.cols; x++) {
for (int c = 0; c < showImage.channels(); c++) {
showImage.at(y, x)[c] =cv::saturate_cast(alpha *
showImage.at(y, x)[c] + beta);
}
}
}
showImage.convertTo(showImage, CV_8UC3);
cv::imshow("Changing the contrast and brightness of an image! ", showImage);
cv::waitKey(0);
based on
[https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB)
[https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7)
#
Advanced subpixel techniques: Shift image content
sub-pixel, floating points, mesh grid, remap, more precise, real-valued
coordinates, moving image pixel, Shift image content with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
##
##
Mesh grid float
static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
mesh grid int
static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
main:
cv::Mat1f XF, YF;
//for int
//meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF,
YF);
//for float
meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF,
YF);
for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) {
for (int colsImage = 0; colsImage < cols_main; ++colsImage) {
XF.at(rowsImage, colsImage) += offset1;
YF.at(rowsImage, colsImage) += offset2;
}
}
cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR);
if (show)
{
cv::Mat resizedImage = dst.clone();
dst.convertTo(resizedImage, CV_8UC3);
cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5);
std::string nameWindow = " meshgrid and remap in float ";
cv::imshow(nameWindow, resizedImage);
cv::waitKey(0);
}
#
Tips and Tricks of OpenCV that Nobody Told You
Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
###
Tricks
cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color
cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale
//copy small Mat to bigger Mat
cv::Rect roi( cv::Point( originX, originY ), smallImage.size() );
smallImage.copyTo( bigImage( roi ) );
###
Tips
* copy mat to vector need clone()
###
###
Save results
* save image in float
* cv::imwrite("image.exr", MatImage);
* save image in uncompressed format :
* cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 });
* * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate
* cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save images in streaming
* int64 t0 = cv::getTickCount();
* std::string fileName= "fashid_"+std::to_string(t0)+ ".png";
* cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save file name:
* std::filesystem::path p = std::filesystem::path(files[i]).filename();
* std::string imgFile = savePath + "/" \+ p.string() + ".tiff";
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ;
###
Error
* Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000.
* check the size of Mat
* cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F);
* cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F);
* Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease'
* the link files are not match based on release or debug mode.
#
Testing for OpenCV Projects
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
* Arrange, Act and Assert (AAA) Pattern
* Google C++ Test Framework
* Assertions Types and Test Fixtures
*
ASSERT_FALSE(frame.empty());
ASSERT_NO_THROW(cap >> img);
ASSERT_FALSE(img.empty()) << "idx=" << idx;
###
Tricks
embedded system, keep the software as small as possible, Embedding static
elements in your application,
[https://gstreamer.freedesktop.org/documentation/installing/index.html?gi-
language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi-
language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y)
###
Tips
* copy mat to vector need clone()
###
###
Example
#include
assert(!im.empty());
assert(x.size()==y.size());
assert(x.size()>2);
#ifdef _DEBUG
#endif
#if true
#else
#endif
#
YouTube
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
[https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u)
A set of C++ APIs are provided to mimic the same behaviors as the MATLAB
function "linspace" and "meshgrid".
Must-Read Books of All Time in Computer Vision and Machine Learning
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# OpenCV
Download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
# [C++](/topics-and-projects/source-code/opencv/cpp)
# [Python](/topics-and-projects/source-code/opencv/python)
NuGet - OpenCV 5 beta
cvtest: Computer Vision Test
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Standard test for computer vision application
Advanced OpenCV techniques:
Advanced OpenCV techniques:
Advanced OpenCV techniques: balance white
Advanced OpenCV techniques: contrast and brightness
Advanced subpixel techniques: Shift image content
Mesh grid float
mesh grid int
main:
Tips and Tricks of OpenCV that Nobody Told You
Tricks
Tips
Save results
Error
Testing for OpenCV Projects
Tricks
Tips
Example
YouTube
#
NuGet - OpenCV 5 beta
NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
#OpenCV #Farshid_PirahanSiah #pirahansiah
My NuGet packages comprised of two versions for different VS version.
* Visual Studio 2019
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9)
* Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7
* Visual Studio 2022
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl)
* Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7
_**more:**_[ _
**https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
_ ****_
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present
"Open Presentation, cvtest in new window")
cvtest
#
Advanced OpenCV techniques:
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
#
Advanced OpenCV techniques:
Cross correlation (CC): TM_CCORR
Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF
Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED
maximum absolute difference metric (MaxAD), which is also known as the uniform
distance metric computeECC() and findTransformECC().
Sum of absolute differences (SAD)
Cross correlation (CC)
find identical regions of an image that match a template, select by giving a
threshold
2D convolution
It simply slides the template image over the input image (as in 2D
convolution) and compares the template and patch of input image under the
template image.
Template matching
> cv2.TM_CCOEFF
> cv2.TM_CCOEFF_NORMED
> cv2.TM_CCORR
> cv2.TM_CCORR_NORMED
< cv2.TM_SQDIFF
< cv2.TM_SQDIFF_NORMED
cv2.minMaxLoc()
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM)
[https://stackoverflow.com/questions/58158129/understanding-and-evaluating-
template-matching-
methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding-
and-evaluating-template-matching-
methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY)
#
Advanced OpenCV techniques: balance white
balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
void balance_white(cv::Mat mat) {
double discard_ratio = 0.05;
int hists[3][256];
memset(hists, 0, 3 * 256 * sizeof(int));
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
hists[j][ptr[x * 3 + j]] += 1;
}
}
}
// cumulative hist
int total = mat.cols * mat.rows;
int vmin[3], vmax[3];
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 255; ++j) {
hists[i][j + 1] += hists[i][j];
}
vmin[i] = 0;
vmax[i] = 255;
while (hists[i][vmin[i]] < discard_ratio * total)
vmin[i] += 1;
while (hists[i][vmax[i]] > (1 - discard_ratio) * total)
vmax[i] -= 1;
if (vmax[i] < 255 - 1)
vmax[i] += 1;
}
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
int val = ptr[x * 3 + j];
if (val < vmin[j])
val = vmin[j];
if (val > vmax[j])
val = vmax[j];
ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] -
vmin[j]));
}
}
}
}
reference http://www.ipol.im/pub/art/2011/llmps-scb/
https://gist.github.com/tomykaira/94472e9f4921ec2cf582
#
Advanced OpenCV techniques: contrast and brightness
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
showImage.convertTo(showImage, CV_32FC3);
double alpha = 2.0; /*< Simple contrast control */
int beta = 100; /*< Simple brightness control */
for (int y = 0; y < showImage.rows; y++) {
for (int x = 0; x < showImage.cols; x++) {
for (int c = 0; c < showImage.channels(); c++) {
showImage.at(y, x)[c] =cv::saturate_cast(alpha *
showImage.at(y, x)[c] + beta);
}
}
}
showImage.convertTo(showImage, CV_8UC3);
cv::imshow("Changing the contrast and brightness of an image! ", showImage);
cv::waitKey(0);
based on
[https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB)
[https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7)
#
Advanced subpixel techniques: Shift image content
sub-pixel, floating points, mesh grid, remap, more precise, real-valued
coordinates, moving image pixel, Shift image content with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
##
##
Mesh grid float
static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
mesh grid int
static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
main:
cv::Mat1f XF, YF;
//for int
//meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF,
YF);
//for float
meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF,
YF);
for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) {
for (int colsImage = 0; colsImage < cols_main; ++colsImage) {
XF.at(rowsImage, colsImage) += offset1;
YF.at(rowsImage, colsImage) += offset2;
}
}
cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR);
if (show)
{
cv::Mat resizedImage = dst.clone();
dst.convertTo(resizedImage, CV_8UC3);
cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5);
std::string nameWindow = " meshgrid and remap in float ";
cv::imshow(nameWindow, resizedImage);
cv::waitKey(0);
}
#
Tips and Tricks of OpenCV that Nobody Told You
Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
###
Tricks
cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color
cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale
//copy small Mat to bigger Mat
cv::Rect roi( cv::Point( originX, originY ), smallImage.size() );
smallImage.copyTo( bigImage( roi ) );
###
Tips
* copy mat to vector need clone()
###
###
Save results
* save image in float
* cv::imwrite("image.exr", MatImage);
* save image in uncompressed format :
* cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 });
* * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate
* cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save images in streaming
* int64 t0 = cv::getTickCount();
* std::string fileName= "fashid_"+std::to_string(t0)+ ".png";
* cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save file name:
* std::filesystem::path p = std::filesystem::path(files[i]).filename();
* std::string imgFile = savePath + "/" \+ p.string() + ".tiff";
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ;
###
Error
* Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000.
* check the size of Mat
* cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F);
* cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F);
* Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease'
* the link files are not match based on release or debug mode.
#
Testing for OpenCV Projects
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
* Arrange, Act and Assert (AAA) Pattern
* Google C++ Test Framework
* Assertions Types and Test Fixtures
*
ASSERT_FALSE(frame.empty());
ASSERT_NO_THROW(cap >> img);
ASSERT_FALSE(img.empty()) << "idx=" << idx;
###
Tricks
embedded system, keep the software as small as possible, Embedding static
elements in your application,
[https://gstreamer.freedesktop.org/documentation/installing/index.html?gi-
language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi-
language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y)
###
Tips
* copy mat to vector need clone()
###
###
Example
#include
assert(!im.empty());
assert(x.size()==y.size());
assert(x.size()>2);
#ifdef _DEBUG
#endif
#if true
#else
#endif
#
YouTube
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
[https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u)
A set of C++ APIs are provided to mimic the same behaviors as the MATLAB
function "linspace" and "meshgrid".
Must-Read Books of All Time in Computer Vision and Machine Learning
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# OpenCV
Download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
# [C++](/topics-and-projects/source-code/opencv/cpp)
# [Python](/topics-and-projects/source-code/opencv/python)
NuGet - OpenCV 5 beta
cvtest: Computer Vision Test
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Standard test for computer vision application
Advanced OpenCV techniques:
Advanced OpenCV techniques:
Advanced OpenCV techniques: balance white
Advanced OpenCV techniques: contrast and brightness
Advanced subpixel techniques: Shift image content
Mesh grid float
mesh grid int
main:
Tips and Tricks of OpenCV that Nobody Told You
Tricks
Tips
Save results
Error
Testing for OpenCV Projects
Tricks
Tips
Example
YouTube
#
NuGet - OpenCV 5 beta
NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
#OpenCV #Farshid_PirahanSiah #pirahansiah
My NuGet packages comprised of two versions for different VS version.
* Visual Studio 2019
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9)
* Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7
* Visual Studio 2022
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl)
* Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7
_**more:**_[ _
**https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
_ ****_
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present
"Open Presentation, cvtest in new window")
cvtest
#
Advanced OpenCV techniques:
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
#
Advanced OpenCV techniques:
Cross correlation (CC): TM_CCORR
Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF
Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED
maximum absolute difference metric (MaxAD), which is also known as the uniform
distance metric computeECC() and findTransformECC().
Sum of absolute differences (SAD)
Cross correlation (CC)
find identical regions of an image that match a template, select by giving a
threshold
2D convolution
It simply slides the template image over the input image (as in 2D
convolution) and compares the template and patch of input image under the
template image.
Template matching
> cv2.TM_CCOEFF
> cv2.TM_CCOEFF_NORMED
> cv2.TM_CCORR
> cv2.TM_CCORR_NORMED
< cv2.TM_SQDIFF
< cv2.TM_SQDIFF_NORMED
cv2.minMaxLoc()
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM)
[https://stackoverflow.com/questions/58158129/understanding-and-evaluating-
template-matching-
methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding-
and-evaluating-template-matching-
methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY)
#
Advanced OpenCV techniques: balance white
balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
void balance_white(cv::Mat mat) {
double discard_ratio = 0.05;
int hists[3][256];
memset(hists, 0, 3 * 256 * sizeof(int));
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
hists[j][ptr[x * 3 + j]] += 1;
}
}
}
// cumulative hist
int total = mat.cols * mat.rows;
int vmin[3], vmax[3];
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 255; ++j) {
hists[i][j + 1] += hists[i][j];
}
vmin[i] = 0;
vmax[i] = 255;
while (hists[i][vmin[i]] < discard_ratio * total)
vmin[i] += 1;
while (hists[i][vmax[i]] > (1 - discard_ratio) * total)
vmax[i] -= 1;
if (vmax[i] < 255 - 1)
vmax[i] += 1;
}
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
int val = ptr[x * 3 + j];
if (val < vmin[j])
val = vmin[j];
if (val > vmax[j])
val = vmax[j];
ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] -
vmin[j]));
}
}
}
}
reference http://www.ipol.im/pub/art/2011/llmps-scb/
https://gist.github.com/tomykaira/94472e9f4921ec2cf582
#
Advanced OpenCV techniques: contrast and brightness
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
showImage.convertTo(showImage, CV_32FC3);
double alpha = 2.0; /*< Simple contrast control */
int beta = 100; /*< Simple brightness control */
for (int y = 0; y < showImage.rows; y++) {
for (int x = 0; x < showImage.cols; x++) {
for (int c = 0; c < showImage.channels(); c++) {
showImage.at(y, x)[c] =cv::saturate_cast(alpha *
showImage.at(y, x)[c] + beta);
}
}
}
showImage.convertTo(showImage, CV_8UC3);
cv::imshow("Changing the contrast and brightness of an image! ", showImage);
cv::waitKey(0);
based on
[https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB)
[https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7)
#
Advanced subpixel techniques: Shift image content
sub-pixel, floating points, mesh grid, remap, more precise, real-valued
coordinates, moving image pixel, Shift image content with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
##
##
Mesh grid float
static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
mesh grid int
static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
main:
cv::Mat1f XF, YF;
//for int
//meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF,
YF);
//for float
meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF,
YF);
for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) {
for (int colsImage = 0; colsImage < cols_main; ++colsImage) {
XF.at(rowsImage, colsImage) += offset1;
YF.at(rowsImage, colsImage) += offset2;
}
}
cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR);
if (show)
{
cv::Mat resizedImage = dst.clone();
dst.convertTo(resizedImage, CV_8UC3);
cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5);
std::string nameWindow = " meshgrid and remap in float ";
cv::imshow(nameWindow, resizedImage);
cv::waitKey(0);
}
#
Tips and Tricks of OpenCV that Nobody Told You
Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
###
Tricks
cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color
cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale
//copy small Mat to bigger Mat
cv::Rect roi( cv::Point( originX, originY ), smallImage.size() );
smallImage.copyTo( bigImage( roi ) );
###
Tips
* copy mat to vector need clone()
###
###
Save results
* save image in float
* cv::imwrite("image.exr", MatImage);
* save image in uncompressed format :
* cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 });
* * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate
* cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save images in streaming
* int64 t0 = cv::getTickCount();
* std::string fileName= "fashid_"+std::to_string(t0)+ ".png";
* cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save file name:
* std::filesystem::path p = std::filesystem::path(files[i]).filename();
* std::string imgFile = savePath + "/" \+ p.string() + ".tiff";
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ;
###
Error
* Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000.
* check the size of Mat
* cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F);
* cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F);
* Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease'
* the link files are not match based on release or debug mode.
#
Testing for OpenCV Projects
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
* Arrange, Act and Assert (AAA) Pattern
* Google C++ Test Framework
* Assertions Types and Test Fixtures
*
ASSERT_FALSE(frame.empty());
ASSERT_NO_THROW(cap >> img);
ASSERT_FALSE(img.empty()) << "idx=" << idx;
###
Tricks
embedded system, keep the software as small as possible, Embedding static
elements in your application,
[https://gstreamer.freedesktop.org/documentation/installing/index.html?gi-
language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi-
language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y)
###
Tips
* copy mat to vector need clone()
###
###
Example
#include
assert(!im.empty());
assert(x.size()==y.size());
assert(x.size()>2);
#ifdef _DEBUG
#endif
#if true
#else
#endif
#
YouTube
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
[https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u)
A set of C++ APIs are provided to mimic the same behaviors as the MATLAB
function "linspace" and "meshgrid".
Must-Read Books of All Time in Computer Vision and Machine Learning
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# OpenCV
Download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
# [C++](/topics-and-projects/source-code/opencv/cpp)
# [Python](/topics-and-projects/source-code/opencv/python)
NuGet - OpenCV 5 beta
cvtest: Computer Vision Test
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Standard test for computer vision application
Advanced OpenCV techniques:
Advanced OpenCV techniques:
Advanced OpenCV techniques: balance white
Advanced OpenCV techniques: contrast and brightness
Advanced subpixel techniques: Shift image content
Mesh grid float
mesh grid int
main:
Tips and Tricks of OpenCV that Nobody Told You
Tricks
Tips
Save results
Error
Testing for OpenCV Projects
Tricks
Tips
Example
YouTube
#
NuGet - OpenCV 5 beta
NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
#OpenCV #Farshid_PirahanSiah #pirahansiah
My NuGet packages comprised of two versions for different VS version.
* Visual Studio 2019
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9)
* Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7
* Visual Studio 2022
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl)
* Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7
_**more:**_[ _
**https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
_ ****_
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present
"Open Presentation, cvtest in new window")
cvtest
#
Advanced OpenCV techniques:
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
#
Advanced OpenCV techniques:
Cross correlation (CC): TM_CCORR
Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF
Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED
maximum absolute difference metric (MaxAD), which is also known as the uniform
distance metric computeECC() and findTransformECC().
Sum of absolute differences (SAD)
Cross correlation (CC)
find identical regions of an image that match a template, select by giving a
threshold
2D convolution
It simply slides the template image over the input image (as in 2D
convolution) and compares the template and patch of input image under the
template image.
Template matching
> cv2.TM_CCOEFF
> cv2.TM_CCOEFF_NORMED
> cv2.TM_CCORR
> cv2.TM_CCORR_NORMED
< cv2.TM_SQDIFF
< cv2.TM_SQDIFF_NORMED
cv2.minMaxLoc()
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM)
[https://stackoverflow.com/questions/58158129/understanding-and-evaluating-
template-matching-
methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding-
and-evaluating-template-matching-
methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY)
#
Advanced OpenCV techniques: balance white
balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
void balance_white(cv::Mat mat) {
double discard_ratio = 0.05;
int hists[3][256];
memset(hists, 0, 3 * 256 * sizeof(int));
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
hists[j][ptr[x * 3 + j]] += 1;
}
}
}
// cumulative hist
int total = mat.cols * mat.rows;
int vmin[3], vmax[3];
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 255; ++j) {
hists[i][j + 1] += hists[i][j];
}
vmin[i] = 0;
vmax[i] = 255;
while (hists[i][vmin[i]] < discard_ratio * total)
vmin[i] += 1;
while (hists[i][vmax[i]] > (1 - discard_ratio) * total)
vmax[i] -= 1;
if (vmax[i] < 255 - 1)
vmax[i] += 1;
}
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
int val = ptr[x * 3 + j];
if (val < vmin[j])
val = vmin[j];
if (val > vmax[j])
val = vmax[j];
ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] -
vmin[j]));
}
}
}
}
reference http://www.ipol.im/pub/art/2011/llmps-scb/
https://gist.github.com/tomykaira/94472e9f4921ec2cf582
#
Advanced OpenCV techniques: contrast and brightness
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
showImage.convertTo(showImage, CV_32FC3);
double alpha = 2.0; /*< Simple contrast control */
int beta = 100; /*< Simple brightness control */
for (int y = 0; y < showImage.rows; y++) {
for (int x = 0; x < showImage.cols; x++) {
for (int c = 0; c < showImage.channels(); c++) {
showImage.at(y, x)[c] =cv::saturate_cast(alpha *
showImage.at(y, x)[c] + beta);
}
}
}
showImage.convertTo(showImage, CV_8UC3);
cv::imshow("Changing the contrast and brightness of an image! ", showImage);
cv::waitKey(0);
based on
[https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB)
[https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7)
#
Advanced subpixel techniques: Shift image content
sub-pixel, floating points, mesh grid, remap, more precise, real-valued
coordinates, moving image pixel, Shift image content with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
##
##
Mesh grid float
static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
mesh grid int
static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
main:
cv::Mat1f XF, YF;
//for int
//meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF,
YF);
//for float
meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF,
YF);
for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) {
for (int colsImage = 0; colsImage < cols_main; ++colsImage) {
XF.at(rowsImage, colsImage) += offset1;
YF.at(rowsImage, colsImage) += offset2;
}
}
cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR);
if (show)
{
cv::Mat resizedImage = dst.clone();
dst.convertTo(resizedImage, CV_8UC3);
cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5);
std::string nameWindow = " meshgrid and remap in float ";
cv::imshow(nameWindow, resizedImage);
cv::waitKey(0);
}
#
Tips and Tricks of OpenCV that Nobody Told You
Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
###
Tricks
cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color
cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale
//copy small Mat to bigger Mat
cv::Rect roi( cv::Point( originX, originY ), smallImage.size() );
smallImage.copyTo( bigImage( roi ) );
###
Tips
* copy mat to vector need clone()
###
###
Save results
* save image in float
* cv::imwrite("image.exr", MatImage);
* save image in uncompressed format :
* cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 });
* * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate
* cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save images in streaming
* int64 t0 = cv::getTickCount();
* std::string fileName= "fashid_"+std::to_string(t0)+ ".png";
* cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save file name:
* std::filesystem::path p = std::filesystem::path(files[i]).filename();
* std::string imgFile = savePath + "/" \+ p.string() + ".tiff";
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ;
###
Error
* Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000.
* check the size of Mat
* cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F);
* cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F);
* Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease'
* the link files are not match based on release or debug mode.
#
Testing for OpenCV Projects
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
* Arrange, Act and Assert (AAA) Pattern
* Google C++ Test Framework
* Assertions Types and Test Fixtures
*
ASSERT_FALSE(frame.empty());
ASSERT_NO_THROW(cap >> img);
ASSERT_FALSE(img.empty()) << "idx=" << idx;
###
Tricks
embedded system, keep the software as small as possible, Embedding static
elements in your application,
[https://gstreamer.freedesktop.org/documentation/installing/index.html?gi-
language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi-
language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y)
###
Tips
* copy mat to vector need clone()
###
###
Example
#include
assert(!im.empty());
assert(x.size()==y.size());
assert(x.size()>2);
#ifdef _DEBUG
#endif
#if true
#else
#endif
#
YouTube
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
[https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u)
A set of C++ APIs are provided to mimic the same behaviors as the MATLAB
function "linspace" and "meshgrid".
Must-Read Books of All Time in Computer Vision and Machine Learning
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# OpenCV
Download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
# [C++](/topics-and-projects/source-code/opencv/cpp)
# [Python](/topics-and-projects/source-code/opencv/python)
NuGet - OpenCV 5 beta
cvtest: Computer Vision Test
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Standard test for computer vision application
Advanced OpenCV techniques:
Advanced OpenCV techniques:
Advanced OpenCV techniques: balance white
Advanced OpenCV techniques: contrast and brightness
Advanced subpixel techniques: Shift image content
Mesh grid float
mesh grid int
main:
Tips and Tricks of OpenCV that Nobody Told You
Tricks
Tips
Save results
Error
Testing for OpenCV Projects
Tricks
Tips
Example
YouTube
#
NuGet - OpenCV 5 beta
NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
#OpenCV #Farshid_PirahanSiah #pirahansiah
My NuGet packages comprised of two versions for different VS version.
* Visual Studio 2019
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9)
* Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7
* Visual Studio 2022
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl)
* Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7
_**more:**_[ _
**https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
_ ****_
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present
"Open Presentation, cvtest in new window")
cvtest
#
Advanced OpenCV techniques:
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
#
Advanced OpenCV techniques:
Cross correlation (CC): TM_CCORR
Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF
Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED
maximum absolute difference metric (MaxAD), which is also known as the uniform
distance metric computeECC() and findTransformECC().
Sum of absolute differences (SAD)
Cross correlation (CC)
find identical regions of an image that match a template, select by giving a
threshold
2D convolution
It simply slides the template image over the input image (as in 2D
convolution) and compares the template and patch of input image under the
template image.
Template matching
> cv2.TM_CCOEFF
> cv2.TM_CCOEFF_NORMED
> cv2.TM_CCORR
> cv2.TM_CCORR_NORMED
< cv2.TM_SQDIFF
< cv2.TM_SQDIFF_NORMED
cv2.minMaxLoc()
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM)
[https://stackoverflow.com/questions/58158129/understanding-and-evaluating-
template-matching-
methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding-
and-evaluating-template-matching-
methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY)
#
Advanced OpenCV techniques: balance white
balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
void balance_white(cv::Mat mat) {
double discard_ratio = 0.05;
int hists[3][256];
memset(hists, 0, 3 * 256 * sizeof(int));
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
hists[j][ptr[x * 3 + j]] += 1;
}
}
}
// cumulative hist
int total = mat.cols * mat.rows;
int vmin[3], vmax[3];
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 255; ++j) {
hists[i][j + 1] += hists[i][j];
}
vmin[i] = 0;
vmax[i] = 255;
while (hists[i][vmin[i]] < discard_ratio * total)
vmin[i] += 1;
while (hists[i][vmax[i]] > (1 - discard_ratio) * total)
vmax[i] -= 1;
if (vmax[i] < 255 - 1)
vmax[i] += 1;
}
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
int val = ptr[x * 3 + j];
if (val < vmin[j])
val = vmin[j];
if (val > vmax[j])
val = vmax[j];
ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] -
vmin[j]));
}
}
}
}
reference http://www.ipol.im/pub/art/2011/llmps-scb/
https://gist.github.com/tomykaira/94472e9f4921ec2cf582
#
Advanced OpenCV techniques: contrast and brightness
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
showImage.convertTo(showImage, CV_32FC3);
double alpha = 2.0; /*< Simple contrast control */
int beta = 100; /*< Simple brightness control */
for (int y = 0; y < showImage.rows; y++) {
for (int x = 0; x < showImage.cols; x++) {
for (int c = 0; c < showImage.channels(); c++) {
showImage.at(y, x)[c] =cv::saturate_cast(alpha *
showImage.at(y, x)[c] + beta);
}
}
}
showImage.convertTo(showImage, CV_8UC3);
cv::imshow("Changing the contrast and brightness of an image! ", showImage);
cv::waitKey(0);
based on
[https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB)
[https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7)
#
Advanced subpixel techniques: Shift image content
sub-pixel, floating points, mesh grid, remap, more precise, real-valued
coordinates, moving image pixel, Shift image content with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
##
##
Mesh grid float
static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
mesh grid int
static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
main:
cv::Mat1f XF, YF;
//for int
//meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF,
YF);
//for float
meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF,
YF);
for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) {
for (int colsImage = 0; colsImage < cols_main; ++colsImage) {
XF.at(rowsImage, colsImage) += offset1;
YF.at(rowsImage, colsImage) += offset2;
}
}
cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR);
if (show)
{
cv::Mat resizedImage = dst.clone();
dst.convertTo(resizedImage, CV_8UC3);
cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5);
std::string nameWindow = " meshgrid and remap in float ";
cv::imshow(nameWindow, resizedImage);
cv::waitKey(0);
}
#
Tips and Tricks of OpenCV that Nobody Told You
Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
###
Tricks
cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color
cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale
//copy small Mat to bigger Mat
cv::Rect roi( cv::Point( originX, originY ), smallImage.size() );
smallImage.copyTo( bigImage( roi ) );
###
Tips
* copy mat to vector need clone()
###
###
Save results
* save image in float
* cv::imwrite("image.exr", MatImage);
* save image in uncompressed format :
* cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 });
* * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate
* cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save images in streaming
* int64 t0 = cv::getTickCount();
* std::string fileName= "fashid_"+std::to_string(t0)+ ".png";
* cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save file name:
* std::filesystem::path p = std::filesystem::path(files[i]).filename();
* std::string imgFile = savePath + "/" \+ p.string() + ".tiff";
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ;
###
Error
* Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000.
* check the size of Mat
* cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F);
* cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F);
* Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease'
* the link files are not match based on release or debug mode.
#
Testing for OpenCV Projects
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
* Arrange, Act and Assert (AAA) Pattern
* Google C++ Test Framework
* Assertions Types and Test Fixtures
*
ASSERT_FALSE(frame.empty());
ASSERT_NO_THROW(cap >> img);
ASSERT_FALSE(img.empty()) << "idx=" << idx;
###
Tricks
embedded system, keep the software as small as possible, Embedding static
elements in your application,
[https://gstreamer.freedesktop.org/documentation/installing/index.html?gi-
language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi-
language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y)
###
Tips
* copy mat to vector need clone()
###
###
Example
#include
assert(!im.empty());
assert(x.size()==y.size());
assert(x.size()>2);
#ifdef _DEBUG
#endif
#if true
#else
#endif
#
YouTube
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
[https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u)
A set of C++ APIs are provided to mimic the same behaviors as the MATLAB
function "linspace" and "meshgrid".
Must-Read Books of All Time in Computer Vision and Machine Learning
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# OpenCV
Download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
# [C++](/topics-and-projects/source-code/opencv/cpp)
# [Python](/topics-and-projects/source-code/opencv/python)
NuGet - OpenCV 5 beta
cvtest: Computer Vision Test
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Standard test for computer vision application
Advanced OpenCV techniques:
Advanced OpenCV techniques:
Advanced OpenCV techniques: balance white
Advanced OpenCV techniques: contrast and brightness
Advanced subpixel techniques: Shift image content
Mesh grid float
mesh grid int
main:
Tips and Tricks of OpenCV that Nobody Told You
Tricks
Tips
Save results
Error
Testing for OpenCV Projects
Tricks
Tips
Example
YouTube
#
NuGet - OpenCV 5 beta
NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
Install and setup your OpenCV project in just 5 minutes
Config your visual studio project for computer vision application
static opencv library for visual studio 2022 by using NuGet package manager
just in few minutes
#YouTube #OpenCV5 #cpp #vs22
Test, C++, Computer Vision, Image Processing,
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
#OpenCV #Farshid_PirahanSiah #pirahansiah
My NuGet packages comprised of two versions for different VS version.
* Visual Studio 2019
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9)
* Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7
* Visual Studio 2022
* [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl)
* Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7
_**more:**_[ _
**https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
_ ****_
#
cvtest: Computer Vision Test
##
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision
and Deep Learning
Do you want to test your output of computer vision application which is video
or images?
##
Standard test for computer vision application
There isn't any standard test for computer vision program. I wrote many test
by myself and I would like to share some of them here. For example, I write a
program to test docker and check the processing time, memory usage, CPU usage,
etc. In computer vision application sometime you need to check the output
which is the image. How do you want to check it. I write some program to check
the output which is the image and compare the ground truth. I check some well
known methods such as PSNR, SSIM, Image quality, distortion, brightness,
sharpness, etc. Furthermore, I check much different hardware and write some
test for computer vision application base on different hardware architecture
and Evaluation hardware.
Do you want to know your program Automatically adjusting brightness of image
in the right way?, How do you know using generic sharpening kernel to remove
blurriness is working?, How to do check FPS process?, Which OCR system work
better for your input image?
[https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil)
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present
"Open Presentation, cvtest in new window")
cvtest
#
Advanced OpenCV techniques:
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
download source code (GitHub):
[https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv-
cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj)
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
#
Advanced OpenCV techniques:
Cross correlation (CC): TM_CCORR
Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF
Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED
maximum absolute difference metric (MaxAD), which is also known as the uniform
distance metric computeECC() and findTransformECC().
Sum of absolute differences (SAD)
Cross correlation (CC)
find identical regions of an image that match a template, select by giving a
threshold
2D convolution
It simply slides the template image over the input image (as in 2D
convolution) and compares the template and patch of input image under the
template image.
Template matching
> cv2.TM_CCOEFF
> cv2.TM_CCOEFF_NORMED
> cv2.TM_CCORR
> cv2.TM_CCORR_NORMED
< cv2.TM_SQDIFF
< cv2.TM_SQDIFF_NORMED
cv2.minMaxLoc()
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
[https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM)
[https://stackoverflow.com/questions/58158129/understanding-and-evaluating-
template-matching-
methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding-
and-evaluating-template-matching-
methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY)
#
Advanced OpenCV techniques: balance white
balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
void balance_white(cv::Mat mat) {
double discard_ratio = 0.05;
int hists[3][256];
memset(hists, 0, 3 * 256 * sizeof(int));
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
hists[j][ptr[x * 3 + j]] += 1;
}
}
}
// cumulative hist
int total = mat.cols * mat.rows;
int vmin[3], vmax[3];
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 255; ++j) {
hists[i][j + 1] += hists[i][j];
}
vmin[i] = 0;
vmax[i] = 255;
while (hists[i][vmin[i]] < discard_ratio * total)
vmin[i] += 1;
while (hists[i][vmax[i]] > (1 - discard_ratio) * total)
vmax[i] -= 1;
if (vmax[i] < 255 - 1)
vmax[i] += 1;
}
for (int y = 0; y < mat.rows; ++y) {
uchar* ptr = mat.ptr(y);
for (int x = 0; x < mat.cols; ++x) {
for (int j = 0; j < 3; ++j) {
int val = ptr[x * 3 + j];
if (val < vmin[j])
val = vmin[j];
if (val > vmax[j])
val = vmax[j];
ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] -
vmin[j]));
}
}
}
}
reference http://www.ipol.im/pub/art/2011/llmps-scb/
https://gist.github.com/tomykaira/94472e9f4921ec2cf582
#
Advanced OpenCV techniques: contrast and brightness
sub-pixel, floating points, more precise, real-valued coordinates, Changing
the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
showImage.convertTo(showImage, CV_32FC3);
double alpha = 2.0; /*< Simple contrast control */
int beta = 100; /*< Simple brightness control */
for (int y = 0; y < showImage.rows; y++) {
for (int x = 0; x < showImage.cols; x++) {
for (int c = 0; c < showImage.channels(); c++) {
showImage.at(y, x)[c] =cv::saturate_cast(alpha *
showImage.at(y, x)[c] + beta);
}
}
}
showImage.convertTo(showImage, CV_8UC3);
cv::imshow("Changing the contrast and brightness of an image! ", showImage);
cv::waitKey(0);
based on
[https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB)
[https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7)
#
Advanced subpixel techniques: Shift image content
sub-pixel, floating points, mesh grid, remap, more precise, real-valued
coordinates, moving image pixel, Shift image content with OpenCV, Tips and
Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
##
##
Mesh grid float
static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1f& X, cv::Mat1f& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
mesh grid int
static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X);
cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);
}
static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv,
cv::Mat1i& X, cv::Mat1i& Y)
{
std::vector t_x, t_y;
for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i);
for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i);
meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y);
}
##
main:
cv::Mat1f XF, YF;
//for int
//meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF,
YF);
//for float
meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF,
YF);
for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) {
for (int colsImage = 0; colsImage < cols_main; ++colsImage) {
XF.at(rowsImage, colsImage) += offset1;
YF.at(rowsImage, colsImage) += offset2;
}
}
cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR);
if (show)
{
cv::Mat resizedImage = dst.clone();
dst.convertTo(resizedImage, CV_8UC3);
cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5);
std::string nameWindow = " meshgrid and remap in float ";
cv::imshow(nameWindow, resizedImage);
cv::waitKey(0);
}
#
Tips and Tricks of OpenCV that Nobody Told You
Tips and Tricks of OpenCV that Nobody Told You
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
###
Tricks
cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color
cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale
//copy small Mat to bigger Mat
cv::Rect roi( cv::Point( originX, originY ), smallImage.size() );
smallImage.copyTo( bigImage( roi ) );
###
Tips
* copy mat to vector need clone()
###
###
Save results
* save image in float
* cv::imwrite("image.exr", MatImage);
* save image in uncompressed format :
* cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 });
* * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate
* cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save images in streaming
* int64 t0 = cv::getTickCount();
* std::string fileName= "fashid_"+std::to_string(t0)+ ".png";
* cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 });
* Save file name:
* std::filesystem::path p = std::filesystem::path(files[i]).filename();
* std::string imgFile = savePath + "/" \+ p.string() + ".tiff";
* cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ;
###
Error
* Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000.
* check the size of Mat
* cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F);
* cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F);
* Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease'
* the link files are not match based on release or debug mode.
#
Testing for OpenCV Projects
Test, C++, Computer Vision, Image Processing,
more:
[https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT)
#OpenCV #Farshid_PirahanSiah #pirahansiah
sample code in C++:
* Arrange, Act and Assert (AAA) Pattern
* Google C++ Test Framework
* Assertions Types and Test Fixtures
*
ASSERT_FALSE(frame.empty());
ASSERT_NO_THROW(cap >> img);
ASSERT_FALSE(img.empty()) << "idx=" << idx;
###
Tricks
embedded system, keep the software as small as possible, Embedding static
elements in your application,
[https://gstreamer.freedesktop.org/documentation/installing/index.html?gi-
language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi-
language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y)
###
Tips
* copy mat to vector need clone()
###
###
Example
#include
assert(!im.empty());
assert(x.size()==y.size());
assert(x.size()>2);
#ifdef _DEBUG
#endif
#if true
#else
#endif
#
YouTube
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